esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Device Characterization
### Comparison of Optical Window Size  
In order to demonstrate the improvements of the effective optical window size through ML integration, we mount the pigtailed focuser, which is connected to an 850 nm fiber laser, on a high-precision automatic linear stage, and sweep it across the fabricated PDs with MLs. Figure 12 shows the normalized photo response versus horizontal displacement. The blue line represents the PD without ML, and the orange one represents the PD with ML. Note that the displacement is relative between the center of the focuser and the PD. It can be seen that the effective optical window size, defined by the full width at half maximum (FWHM) of the curves in Figure 12, is increased from \(\sim\)24 \(\upmu\)m (when there is no ML) to \(\sim\)52 \(\upmu\)m (when there is an ML), demonstrating the effectiveness of the integrated ML. Note that such sweepings are performed in both x and y directions to examine the symmetry of the integrated ML, and we find no particular difference between the 2 directions implying good circular symmetry of the integrated ML.
## 2 Review of the Standard PSO and Its Variants
### Some Variants of PSO  
In recent decades, the real-world problems that need to be solved have become increasingly complex. The classical PSO algorithm often shows premature convergence and a poor global search ability. To improve the performance of the classical PSO, some researchers and engineers have proposed plenty of PSO variants. Depending on the objectives to be dealt with, most PSO variants can be divided into four types: parameter tunings, domain topology adjustments, learning strategies, and hybrid strategies, which are briefly reviewed here.  
**(1)** Parameter tuning  
Generally, the parameter control of a PSO mainly focuses on the inertia weight (\(w\)) and acceleration coefficients (\(c_{1}\) and \(c_{2}\)). The inertia weight, used to maintain the direction of particle movement, is a critical parameter of a standard PSO. Reducing the inertia weight over time can significantly improve the performance of the PSO [17]. Now, many PSOs still use this update strategy to linearly reduce \(w\) from 0.9 to 0.4 with the progress of iteration [18; 19; 20; 21]. It has been found through experiments that with a smaller \(w\), the particles move at a smaller pace, which is conducive to exploitation, while with a larger \(w\), particles can appear at a farther place each time, which is more helpful to exploration. However, considering the complexity of the PSO search process, some researchers have introduced nonlinear-varying strategies to assign inertia weights, which is able to endow particles with diverse search behaviors [22; 23; 24; 25; 26; 27].  
Although the above-mentioned inertia weight updating methods improve the performance of the PSO to a certain extent, they are not good enough to match between the parameter adjustments and the search process due to the lack of appropriately used evolutionary state information in the iterative process. To obtain a more satisfactory adjustment for the parameters, the inertia weight can be updated based on some feedback parameters. For instance, in [25], the success rate of the swarm was used as a feedback parameter to adaptively adjust the inertia weight, which can configure a more appropriate value of the inertia weight according to the particles' situation in the search space. In an adaptive PSO (APSO) [28], a systematic adaptation scheme is used to control \(w\), \(c_{1}\), and \(c_{2}\). This scheme is not only based on an evolutionary state estimation, but also considers the influence of these parameters in different evolutionary states. In the artificial multiswarm PSO (AMPSO) [29], an adaptive inertia weight strategy based on a hybrid of position diversity is matched to different subswarms to better balance exploration and exploitation and improve the performance of the algorithm. What these methods have in common is that they can deeply mine the fitness, speed, position, and other information of the particles, which can be effectively used to adjust their own parameters during the evolution process.
# Toward Secure Smart Grid Systems: Risks, Threats, Challenges, and Future Directions
## 9 Lessons Learned, Suggestions, and Recommendations
### Lessons Learned
and Safety Awareness Training: This is especially for users and operators to protect the smart grid systems to avoid risky behaviors, overcome phishing and social and reverse engineering,and raise situational awareness training, encouragement training, and accountability. This requires being aware of how to identify and report suspicious activity, protect sensitive data, and respond to a security incident. **Enhanced Physical Security:** Examples include tamper-resistant equipment, access control, surveillance, and secure storage to thwart theft, vandalism, and sabotage. **Mitigating Insider Threats:** Threats may be mitigated by, e.g., adopting security measures that limit access privileges, offering timed access control, training employees on security best practices, and monitoring smart grid systems, networks, and devices for any suspicious activities before initiating an incident response reaction. **Securing Logistics and Supply Chains:** This is to avoid exploiting hardware and software vulnerabilities, with smart grid systems being tampered with or compromised. This requires obtaining verified software components and equipment from trusted vendors before validating and testing them before integrating them into the smart grid system.
# Evaluation of Fire Blight Resistance of Eleven Apple Rootstocks Grown in Kazakhstan Fields
## 2 Materials and Methods
### Disease Monitoring, Sampling, and DNA Isolation

Table 1: Main characteristics of clonal apple rootstocks. The fields were treated using the systemic fungicide Rayok (Avgust) and the insecticide Movento\({}^{\text{\textregistered}}\) Energy (Bayer, Leverkusen, Germany) in the early spring of 2021 and 2022. Chemicals that act against fire blight were not applied. The rootstock plants were monitored in the field to identify the presence of fire blight infection during 2021 and 2022, taking environmental conditions into consideration. Disease distribution index (DDI) was assessed in mid-July using a 5-point scale according to the following classification: 0--asymptomatic; 1--sporadic symptoms of fire blight in apple rootstock field, less than 10% of affected trees; 2-10% to 20% of rootstock trees affected by pathogen; 3-20% to 50% of the rootstock trees affected by pathogen; 4-50% to 100% of trees damaged by infection [31]. DDI was calculated by dividing fire blight-affected trees on 1,000 randomly examined trees in the field multiplied by 100. The disease severity index (DSI) [31] was calculated using the following formula:  
\[\text{DSI}=\text{(fire blight shoots/total shoots)}\times 100\]  
Seed rootstocks of _Malus sieversii_ were represented by different genotypes collected from the Tian Shan mountains. The researched fields of rootstocks were predominantly located in the southern region of the country. DNA isolation for genetic and pathogen testing was conducted using ten samples of every rootstock type according to the CTAB protocol [32]. Detection of _E.anylovora_ was performed by testing rootstock bark and shoots using the real-time PCR method described by [PERSON] [33].
# Sustainability as a Challenge and Driver for Novel Ecosystem 6G Business Scenarios
## 3 Results
### Communities Scenario 1D
#### 3.2.3 Sustainability Theme #3  
The sustainability theme recognizes the increased environmental awareness and the UN sustainability goals as important drivers for 6G. The scenario's logic is grounded in sustainability development and the configuration of power, as depicted in Figure 6.  
Gaia Scenario 3A  
The Gaia scenario stems from redefined economy and poly-nodal power configuration. Climate change and biodiversity crises have increased green awareness and resulted in empowered actions on the individual and society levels. The emphasis on global politics has moved towards relationships and interaction, replacing the polarized power regime. In the poly-nodal scenario, think tanks, non-profit organizations municipalities, local communities, and increasingly firms are playing a significant role. 6G has enabled online economy, where transactions have been digitalized and automated, impacting productivity across industries. Energy production originating mainly from renewable sources has become increasingly decentralized as people and communities produce and trade energy. Utilizing circular economy and sharing economy business models, production and consumption are managed and orchestrated to minimize waste, while materials and their value flow via sharing, leasing, repair, and reuse. In the innovation to zero context, ultra-low-power 6G communication  
Figure 6: Summary of sustainability scenarios [32].  
leverages innovations in energy harvesting and wireless power to \"next billion devices\". Disruptive business model concepts building on platform cooperatives, peer-to-peer and crowdsourcing, and the progress of the human-driven fair data economy have established counterforces to winner-takes-all monopolies. Furthermore, international co-operation in policies and regulation has played a vital role in regulating platform business. From IoT, focus has moved to the Internet of human skills and senses, utilizing enhanced HMI for restorative economy characterized by empowerment, equality, well-being, and sustainability. Achieved societal resilience provides the ability to cope with the future world with uncertainty, turbulence, rapid change, dynamism, disruption, complexity, hyper-competition, high-velocity markets, and flux. Furthermore, resilience can be seen as the dynamic capability of a society to overcome adversities, the ability to learn and adjust, and the ability to craft sets of institutions that foster individual welfare and sustainable societal robustness to future challenges and crises.
## 3 Results and Discussion
### Effect of Precipitant Type on the Morphology of Alumina Precursors
Due to the poor stability of sodium basic aluminum ammonium carbonate under high-temperature conditions, some of the sodium basic aluminum ammonium carbonate decomposed into boehmite during the crystallization process, resulting in a rough surface on the crystals. This was evidenced by the appearance of characteristic peaks of boehmite in the XRD spectrum, as indicated by the chemical reaction in Equations (3) and (4). \[AlCl_{3}+4 NH_{4}HCO_{3}=NH_{4}Al(OH)_{2}CO_{3}\downarrow+3 NH_{4}Cl+3 CO_{2} \uparrow+H_{2}O \tag{3}\]  
\[NH_{4}Al(OH)_{2}CO_{3}=\gamma\text{-}AlOOH+NH_{3}\text{-}H_{2}O+CO_{2}\uparrow \tag{4}\]  
Sodium bicarbonate and potassium bicarbonate share similar properties. When potassium bicarbonate was used as the precipitant, in the reaction system, potassium bicarbonate first reacted with aluminum chloride to form boehmite. Due to the larger radius of potassium ions, boehmite cannot react with potassium bicarbonate. Therefore, the precursor only contained boehmite as the phase. The characteristic peak of boehmite in the XRD \begin{table}
\begin{tabular}{c c c} \hline \hline
**Precipitants** & **Lattice Parameters (Å)** & **Average Crystalline Size, D (nm)** \\ \hline (a) & NaAl(OH)2 CO3 (a = 3.7, b = 12.2, c = 2.8) & 19.96 \\ (b) & \(\gamma\)-AlOOH (a = 3.7, b = 12.2, c = 2.8) & 20.03 \\ (c) & \(\gamma\)-AlOOH (a = 3.7, b = 12.2, c = 2.8) & 22.53 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Average crystal grain size of aluminum oxide precursors prepared with different precipitants. (a) Sodium bicarbonate; (b) ammonium bicarbonate; (c) potassium bicarbonate. spectrum had a wider half-peak width, indicating a finer crystal particle size. The reaction process is illustrated by the chemical reaction in Equation (5). \[\textit{AICJ}_{3}+3\textit{KHCO}_{3}=\gamma\textit{-AlOOH}\downarrow+3\textit{ KCl}+3\textit{CO}_{2}\uparrow+\textit{H}_{2}O \tag{5}\]  
From Figure 3a, it can be observed that the TG curve sharply decreases starting at 309.8 \({}^{\circ}\)C, indicating the thermal decomposition of carbonate ions. A slight decrease in the TG curve is observed at 711 \({}^{\circ}\)C, which may be attributed to the removal of water molecules with different binding energies in the aluminum oxide precursor. The DTG curve in Figure 3b reveals that the mass change before 279 \({}^{\circ}\)C is due to the removal of adsorbed water and thermal decomposition. After 279 \({}^{\circ}\)C, the mass change is attributed to the removal of crystalline water from the aluminum oxide precursor. As depicted in Figure 3c, the DTG curve shows two peaks at 100 \({}^{\circ}\)C and 300 \({}^{\circ}\)C, which are likely caused by the removal of excess water molecules from the surface and interlayers of boehmite. The sharp decrease in the TG curve at 409 \({}^{\circ}\)C indicates the transformation of boehmite into \(\gamma\)-alumina. Based on the three TG curves, it can be inferred that aluminum aluminate carbonate is more readily converted to \(\alpha\)-alumina compared to boehmite and sodium aluminate carbonate.
# MCIDN: Deblurring Network for Metal Corrosion Images
## 5 Limitations and Future Work  
Although our method achieves high performance on blurry metal corrosion datasets, it is still constrained by the dataset quality, and the restoration effect on certain corrosion images remains suboptimal. Our future work will focus on developing domain-adaptive algorithms to fully exploit the potential of synthetic blurry metal corrosion datasets for real-world applications. These algorithms aim to bridge the gap between synthetic and real-world data, enhancing the generalization capability and robustness of deblurring models when applied to actual corrosion scenarios. This approach has the potential to significantly improve the practicality and effectiveness of image deblurring techniques in industrial environments.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \(L_{s}\) & \(L_{f}\) & \(L_{e}\) & **PSNR** & **SSIM** \\ \hline ✓ & ✗ & ✗ & 32.0136 & 0.9755 \\ ✓ & ✗ & ✓ & 32.2078 & 0.9758 \\ ✓ & ✓ & ✗ & 32.4522 & 0.9763 \\ ✓ & ✓ & ✓ & 32.8645 & 0.9768 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Verify the effectiveness of each loss in MCIDN.  
Figure 10: PSNR and SSIM curves from ablation experiments on different components.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Baseline** & **SCAM** & **FCAM** & **PSNR** & **SSIM** & **Params(M)** & **FLOPs(G)** \\ \hline ✓ & ✗ & ✗ & 31.5345 & 0.9745 & 17.0 & 18.53 \\ ✓ & ✓ & ✗ & 32.3106 & 0.9762 & 22.3 & 18.54 \\ ✓ & ✗ & ✓ & 32.6760 & 0.9767 & 28.2 & 21.02 \\ ✓ & ✓ & ✓ & 32.8645 & 0.9768 & 32.7 & 21.02 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Ablation studies for the proposed modules on blurry metal corrosion dataset.
## 3 Dynamic Response Analysis of the Test Model
### Horizontal Displacement of Measurement Points  
Figure 12 shows that as the peak acceleration of the seismic wave increased, the horizontal displacements of the measurement points at the top of the anti-slide piles and at the bottom of the slope increased, but the horizontal displacement of the cushion cap was zero, and the failure of the slope did not cause the pier pile foundation to have a large flexural deformation. The slope of the horizontal displacement curve at the top of the front-row anti-slide piles was small and developed linearly. With the increase in the vibration intensity, the horizontal displacement curve at the top of the back-row anti-slide piles changed from linear to non-linear. When the peak acceleration of the seismic wave reached 0.4 g, the slope of the curve increased significantly, cracks occurred in the anchoring section of the pile shaft, and cracks continued to propagate under the seismic waves, leading to a substantial increase in the horizontal displacement of the top of the back-row anti-slide piles. The crack locations of 17 back-row anti-slide piles were statistically analyzed. As shown in Figure 13, the blue horizontal line was the position of the sliding surface, and the black curves marked the cracks. The distance between the crack and the sliding surface was approximately one fifth of the anchorage length of the anti-slide piles. The crack location was under the combined action of a large bending moment and shear, resulting in a common form of stress-induced failure of elastic piles.  
Figure 11: Development process of landslide under the action of vibration wave. (**a**) soil slip on the slope surface; (**b**) local failure of the slope body; (**c**) failure of the lower half of the sliding body; (**d**) the test model was completely destroyed.  
Figure 12: Horizontal displacement curves.
## 1 Introduction
The transformation process expresses the structure of the network as an SES tree, selects the specific entities and the environment parameters using our pruning algorithm, and coverts the SES into a pruned entity structure (PES) tree. This process constructs a hierarchical and structural simulation model by combining the tree structure, the parameters, and the atomic DEVS models. Experimental results indicate that our framework improves the execution time by up to 8% and the central processing unit (CPU) utilization cost by up to 23% as compared to the manual model synthesis. The main contributions of this paper are as follows:  
* Applying the SES/MB for synthesizing the structure and models and automating the complicated synthesizing process of WSN modeling construction;
* Saving time and cost in synthesizing various models by proposing a novel transformation process;  
Figure 1: Wireless sensor network. * Achieving efficiency of the WSN-SES/MB framework with a new pruning algorithm in our transformation process;
* Increasing diversity of synthesized simulation models by introducing the WSN-SES/MB framework;
* Facilitating the modeling and simulation of large-scale WSNs with all of the above. The rest of this paper is organized as follows: Section 2 introduces the existing general simulators for sensor networks. Section 3 presents the SES/MB framework for synthesizing the structure and the models. We offer a detailed description of the WSN-SES/MB framework in Section 4. In Section 5, we present a performance evaluation of the WSN-SES/MB framework using analysis and simulation. We draw conclusions at the end of this paper.
## 1 Introduction
The IPD-based BPF was designed and modeled in the form of an equivalent circuit as presented in Section 2.1, and the fabrication process is described in Section 2.2 to clarify the detailed physical structure of the device. The performance description and comparison of the proposed BPF and the published BPFs are presented in Section 3. QFN-packaging technology was used to protect the device from moisture and ensure good thermal and electrical performances. The packaged device was fixed on the center of a printed circuit board (PCB) using gold wire-bonding for final measurement and evaluation.
# Experimental Evaluation of Collision Avoidance Techniques for Collaborative Robots
## 2 Obstacle Avoidance Algorithm
### Mode I: 6-DOF Perturbation  
Mode I leaves the manipulator 6-DOF to avoid the obstacle, varying position and orientation with respect to the planned trajectory. The algorithm is based on the Closed-Loop Inverse Kinematics (CLIK) approach, imposing two-speed tasks. Joint velocities can be calculated as  
\[\mathbf{\dot{q}}=\mathbf{J}^{*}(\dot{\mathbf{x}}+\mathbf{K}\mathbf{e})+\mathbf{J}_{l}^{*}\big{[}a_{v}v_ {rep}\mathbf{\dot{d}}_{0}\big{]} \tag{7}\]  
where \(\dot{\mathbf{x}}\) is the vector of planned velocities and \(\dot{\mathbf{x}}_{0}=a_{v}v_{rep}\mathbf{\dot{d}}_{0}\) is the repulsive velocity due to the presence of an obstacle. \(v_{rep}\) is a tunable magnitude, and \(a_{v}\) is an activation factor that is a function of the distance from the obstacle \(\mathbf{d}_{0}\). The damped least square algorithm is applied to invert \(\mathbf{J}\) and \(\mathbf{J}_{l}=\mathbf{J}_{0p}\). The term \(\mathbf{e}\) is a vector of position and orientation errors (\(\mathbf{e}_{p}\) and \(\mathbf{e}_{r}\)), defined as  
\[\mathbf{e}=\begin{bmatrix}\mathbf{e}_{p}\\ \mathbf{e}_{r}\end{bmatrix}=\begin{bmatrix}\mathbf{P}-\mathbf{P}_{d}\\ \frac{1}{2}\big{(}\mathbf{i}\times\mathbf{i}_{d}+\mathbf{j}\times\mathbf{j}_{d}+\mathbf{k}\times \mathbf{k}_{d}\big{)}\end{bmatrix} \tag{8}\]  
where \(\mathbf{P}\) is the position of the end-effector and the subscript \(d\) represents the desired planned variable. Instead, \(\mathbf{i}\), \(\mathbf{j}\), and \(\mathbf{k}\) are the unit vectors of the end-effector reference frame. In (7), the error \(\mathbf{e}\) is multiplied by \(\mathbf{K}\), which is defined as \(\mathbf{k}_{e}\mathbf{I}\) with \(\mathbf{k}_{e}\) a gain vector that may vary according to the application. It is possible to give different gains to the orientation (\(k_{er}\)) or positioning (\(k_{ep}\)) components of the error: \(\mathbf{k}_{e}=\begin{bmatrix}k_{ep}&k_{ep}&k_{ep}&k_{er}&k_{er}\end{bmatrix}\).
## 2 Design Section
### Design Evolution
#### 2.2.2 The Influence of the Radiation Patch Shape  
Fractal geometry can be used to miniaturize patch antennas by increasing the effective electrical length of the radiator in the limited area. Consequently, fractal structures can be applied to the compact antenna designs, whose radiation patterns and return loss characteristics are similar to those of larger antennas [11, 12].  
The [PERSON] fractal geometry is applied to the radiation patch in this design. The HFSS software was also used for a comparative numerical study between the original rectangular and the proposed fractal geometry radiators, which were both coupled with the CSRR slot. The dimensions of the [PERSON] fractal are the same as those illustrated in Figure 1, and the original radiator patch  
Figure 2: Simulated return loss with different coupling structures.  
is in a square shape of 13.9 mm \(\times\) 13.9 mm. The return loss versus frequency curves of the antenna are illustrated in Figure 3. As shown in Figure 3, the original square radiator resonated at both 4.1 GHz and 4.8 GHz. In comparison, the proposed [PERSON] fractal radiator resonates at both 3.9 GHz and 4.3 GHz. With the [PERSON] fractal geometry, the current of the proposed antenna should be forced to travel along the convoluted path instead of a straight Euclidean path. It can be seen that the decrease of the resonant frequencies of the fractal patch antenna is quite obvious. Furthermore, the fractal antenna has significant improvement of the return loss level at its operating frequencies. It should be noted that the bandwidth at the resonant frequency of the [PERSON] fractal patch is slightly narrower than that of the square patch.
# Robust Registration of Medical Images in the Presence of Spatially-Varying Noise
## 2 Materials and Methods
### Datasets
#### 2.5.1 BrainWeb Dataset  
The BrainWeb dataset [43; 45] contains simulated brain MRI volumes from several protocols, including T1-weighted (MR-T1), T2-weighted (MR-T2), and proton density (MR-PD). Here, we selected 20 two-dimensional slices of the MR-T1 (\(218\times 181\)). The intensities were normalized between 0 and 1.