esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Design of Analytic Method  
To investigate the relationship between the breakdown voltage and various \(\mathrm{N_{BPW}}\), we extracted the BPW charge, BPW junction depth, and depletion width by using a Gaussian distribution and Poisson's equation. The depth profile of \(\mathrm{N_{BPW}}\) was divided into two regions: the box profile region and the Gaussian profile region, as shown in Figure 2.  
The charges for various \(\mathrm{N_{BPW}}\) were calculated using the doping concentration distribution functions:  
\[\mathrm{Q(x)}=\mathrm{R_{p}N_{p}}+\int\mathrm{N_{p}}\exp\left[-\frac{\left( \mathrm{x-R_{p}}\right)^{2}}{2\Delta\mathrm{R_{p}^{2}}}\right]\mathrm{dx} \tag{1}\]  
The charge of the box profile region is approximated by the constant term \(\mathrm{R_{p}N_{p}}\), and that of the Gaussian profile region was calculated using the integral of the Gaussian distribution function term. The BPW junction depth (\(\mathrm{x_{j}}\)) with various \(\mathrm{N_{BPW}}\) was determined by solving the Gaussian distribution function [23]:  
\[\mathrm{N(x)}=\mathrm{N_{p}}\exp\left[-\frac{\left(\mathrm{x-R_{p}}\right)^{2 }}{2\Delta\mathrm{R_{p}^{2}}}\right] \tag{2}\]  
where \(\mathrm{N_{p}}\) is the highest \(\mathrm{N_{BPW}}\), \(\mathrm{R_{p}}\) is the average distance when the doping concentration is \(\mathrm{N_{p}}\), and \(\Delta\mathrm{R_{p}}\) is the projected straggle standard deviation that is empirically used for 4H-SiC implantation data [24]. The BPW junction depth \(\mathrm{x_{j}}\) can be obtained by integrating Equation (1), when the doping concentration is \(\mathrm{N_{epi}}\):  
\[\mathrm{x_{j}}=\mathrm{R_{p}}+\mathrm{\Delta R_{p}}\left[2\log\left(\frac{ \mathrm{N_{p}}}{\mathrm{N_{epi}}}\right)\right] \tag{3}\]  
Figure 2: Depth profile of bottom protection p-well (BPW) doping concentration with \(\mathrm{N_{BPW}}\) of \(1\times 10^{17}\) cm\({}^{-3}\) including box profile and Gaussian profile in the cutline A-A’ section line of the device.  
In addition, the depletion width (W) was dependent on the doping concentrations, such as N\({}_{\text{drift}}\) and N\({}_{\text{BPW}}\). It was calculated using [PERSON]'s equation [13; 21].  
\[\text{W}=\sqrt{\frac{2\epsilon_{\text{s}}\text{v}_{\text{bi}}}{\text{q}}\Bigg{(} \frac{1}{\text{N}_{\text{epi}}}+\frac{1}{\text{N}_{\text{BPW}}}\Bigg{)}} \tag{4}\]  
where \(\epsilon_{\text{s}}\) and q represent the dielectric constant and the charge of the electron, respectively, and v\({}_{\text{bi}}\) is the built-in potential. The junction depth (x\({}_{\text{j}}\)) and the depletion width (W), as functions of N\({}_{\text{BPW}}\), affected the breakdown voltage. Therefore, we propose a breakdown voltage length factor that divides the depletion width by the junction depth. This parameter indicates the degradation of the breakdown voltage when N\({}_{\text{BPW}}\) increases.
## 3 Proposed Method
### Proposed Proof of Familiarity (PoF) Consensus Algorithm  
#### 3.2.1 Functional Architecture Proof of Familiarity (PoF)  
In PoF, decisions are taken based on the individual's level of expertise, experience, and success rate. We firstly posit that D, I\({}_{\rm c}\), and C\({}_{\rm p}\) involved in a particular occupation or experience for a longer period also have a better decision-making ability. We secondly posit that D, I\({}_{\rm c}\), and C\({}_{\rm p}\) already provided a correct decision, also have a higher chance of taking the correct decision in future collaboration. We explore the above two conjectures to propose that if D, I\({}_{\rm c}\), and C\({}_{\rm p}\) provide an opinion or medical decision, the winner is always selected based on the previous two propositions. To express individual qualitative achievements numerically, individual familiarity index (IFI) is calculated. IFI starts at 0.0 and can be up to 1.0. IFI is updated according to the performance history of D, I\({}_{\rm c}\), and C\({}_{\rm p}\) stored in DB\({}_{\rm loc}\) and DB\({}_{\rm bc}\). IFI index of PoF depends on several considering factors:  
* The judgement of the doctor: While choosing a doctor's decision, factors like job experience time (J\({}_{\rm ET}\)) and treatment success rate (T\({}_{\rm SR}\)) are considered. IFI of a doctor (IFI\({}_{\rm D}\)) is calculated as IFI\({}_{\rm D}\) = J\({}_{\rm ET}\), T\({}_{\rm SR}\)].
* The perspective of the cured patient: Proposed system considers factors like treatment experience time (T\({}_{\rm ET}\)), current condition (C\({}_{\rm C}\)), and experience of disease (E\({}_{\rm D}\)) of cured patients. IFI of a cured patient (IFI\({}_{\rm C}\)) is calculated as: IFI\({}_{\rm C}\)\({}_{\rm p}\) = {T\({}_{\rm ET}\), C\({}_{\rm C}\), E\({}_{\rm D}\)}.
* Perception of insurance company: Financial policy is another significant aspect of collaborative medical decision-making. IFI of an insurance company (IFI\({}_{\rm L}\)) is calculated from settlement time (S\({}_{\rm T}\)) and cover amount (C\({}_{\rm A}\)). IFI\({}_{\rm L}\) is calculated as: IFI\({}_{\rm L}\) = {S\({}_{\rm T}\), C\({}_{\rm A}\)}.
* Final collaborative medical decision: Finally, PoF calculates the final collaborative medical decision, C\({}_{\rm d}\). Based on the IFI value (IFL\({}_{\rm c}\), IFI\({}_{\rm C}\)\({}_{\rm p}\), and IFI\({}_{\rm D}\)) of three entities, a winner is chosen from each of the entities (D, I\({}_{\rm c}\), and C\({}_{\rm p}\)). After combining three winner's decisions, C\({}_{\rm d}\) is decided for P.
## 5 Discussion  
To evaluate the effectiveness of the FNN Model at different frequencies induced by temperature and vibration, the pointing errors were analyzed using Fourier coordinates. Because the pointing error is influenced by multiple factors, its amplitude in the Fourier domain is smaller than in the time domain. As shown in Figure 15a,b, the FNN Model demonstrates better performance in reducing elevation errors compared to azimuth errors. This is attributed to the larger initial pointing errors in the azimuth direction, which  
Figure 14: Speed of acquisition process.  
Figure 13: Success rate of the acquisition process.  
provide greater potential for optimization. Additionally, Figure 15c,d show that the original pointing error exhibited a periodicity of 27 s, whereas the adjusted pointing error no longer displays this periodic behavior. This phenomenon may be related to the specific settings of the laser terminal on the satellite.
## 4 Discussion
### Role of Predictive Dosimetry with \({}^{99m}\)Tc-MAA SPECT  
Nowadays, the use of PET/CT systems for \({}^{90}\)Y-us dosimetry purposes is a well established approach since \({}^{90}\)Y PET reflects the tumor heterogeneity better than traditional bremsstrahlung \({}^{90}\)Y SPECT [28, 31, 32]: this is crucial to obtain reliable estimates of \({}^{90}\)Y-us activity distribution, essential for the assessment of the dose delivered to hepatic tumours. However it should be noted that \({}^{90}\)Y-PET can be performed after the administration of microspheres thereby allowing only for post-therapy dosimetry.  
At present, predictive dosimetry with \({}^{99m}\)Tc-MAA SPECT has been demonstrated to improve the average quality of treatment and recent EANM guidelines encourages the use of \({}^{99m}\)Tc-MAA for therapy optimization [33]. However it should be stressed that  
Figure 6: Cumulative DVH comparisons between \({}^{99m}\)Tc SPECT/CT of this study and those (rescaled) reported by [PERSON] et al. in [19] for Monte Carlo (MC), \({}^{90}\)Y-PET/CT (PET/CT), \({}^{90}\)Y-SPECT/CT (Bremsstrahlung) and \({}^{99m}\)Tc SPECT/CT (\({}^{99m}\)Tc) for the AbdoMan inserts (**a**) 40 mm sphere, (**b**) 20 mm sphere, (**c**) 40 mm hollow sphere with 25 mm solid inner sphere, simulating a neovascularization rim around a necrotic core) and (**d**) liver compartment.  
the potential to perform accurate dosimetric treatment planning relies on the predictive accuracy of \({}^{99\text{m}}\)Tc-MAA for \({}^{90}\)Y microsphere distribution and the general consensus is that the decision to treat an individual solely on the bases of the predicted lesion-absorbed dose is a delicate matter [33]. Moreover, the predictive value for dose evaluation based on \({}^{99\text{m}}\)Tc-MAA SPECT is still subject of debate in the literature [20,21].  
Against this backdrop, our study aims to support the use of \({}^{99\text{m}}\)Tc-MAA SPECT acquisition for provisional dosimetry along with the local energy deposition method to convert reconstructed SPECT data into absorbed dose maps. In particular, as long as \({}^{99\text{m}}\)Tc-MAA SPECT reconstructions are related to liver lesions with dimension comparable to the 40 mm in diameter sphere, the absorbed dose computed by means of the local energy deposition method can lead to results in line with those obtained by Monte Carlo computation. Our findings confirm previous research [28]. This is because the tumor dimension exceeds the SPECT system resolution and has the potential to overcome or reduce image artifacts (e.g., PVE). On the other hand, \({}^{99\text{m}}\)Tc-MAA SPECT reconstructions related to small liver lesions (approximately 20 mm) or larger areas presenting an hypoxic necrotic core should be analyzed with care for a correct interpretation in a clinical setting.
# 25-34 GHz Single-Pole, Double-Throw CMOS Switches for a Ka-Band Phased-Array Transceiver
## 4 Conclusions  
In this paper, two SPDT mm-wave switches for Ka-band beamforming applications were proposed, which were fabricated using a 65 nm CMOS process: Two types of switch core circuit, one controlled using a CB technique, and the other by a DSB technique, were also proposed. By connecting an IE inductor for canceling out parasitic capacitances of the switch core at resonance, the isolation performance of the switches can be improved, as well as the insertion loss, without additional shunt switches. The core size of the proposed RF switch, excluding all pads, was 260 \(\upmu\)m \(\times\) 230 \(\upmu\)m. The measured insertion losses of the CB switch and the DSB switch were below 2.3 dB and 2.5 dB, respectively, between 25 GHz and 34 GHz. The measured return losses of the CB and DSB switches were below 16.7 dB and 17.3 dB, and the isolation of the switches was less than 18.4 dB and 15.3 dB, respectively, in the bandwidth. The IIP3s of the switches were 38.4 dBm and 39 dBm, respectively, at 28 GHz.  
This work was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2017R1D1A1B03035656) and the Chung-Ang University Graduate Research Scholarship in 2016.  
[PERSON] conceived and designed the circuit, performed the experiments and wrote the manuscript; [PERSON] and [PERSON] drew the layout of the circuits and analyzed the experimental data. [PERSON] modified the article. [PERSON] participated in research plan development, suggested the idea, and revised the manuscript. All authors have contributed to the manuscript.  
The authors declare no conflict of interest.
## 3 The Proposed Model
### Design of AAA Based FS Technique  
Once the raw data are pre-processed and features are extracted, the AAA-FS model is utilized to choose feature subsets. In 2015, [PERSON] et al. [PERSON] _et al._ (2015) presented AAA, a bio-inspired metaheuristic optimized technique to overcome real-time and continuous optimization issues. It is a stimulation for the search activity of microalgae. Every individual is regarded as an artificial algal community (AAC) from the population-based technique; also, every AAC resembles a solution from the problem space. The life cycle encompasses mitotic reproduction, altering the dominant species, and environmental adaptation. The adaptation stage, the reproduction or evolutionary stage, and the helical movement phase are the three stages of AAA. The evolutionary/reproduction stage is exploited for replenishing the community cell by resurrecting algae by mitotic division when they have enough light and nutrients in the environment. Algae perform a movement named helical motion. The algae population exists in liquid atmosphere and congregates nearer to the liquid surface  
Figure 1: Block diagram of ODAE-WPDC approach.  
wherever there is a sufficient light source. The algae cell uses their flagellum (organelle) for helical motion [24]. Figure 2 depicts the flowchart of AAA and explained in Algorithm 1.  
```
Initialization: Generate \(N\) population of algae colonies Determine fitness \(f(x_{i})\), \(i=1,2,\ldots,N,D\) where \(x_{i}=\) algae colony, N = number of algae colonies, and that the D = problem dimensionality while termination condition is unsatisfied do for \(i=1\) to \(n\) do while energy of \(i^{th}\) colony not done do Employ helical movement stage end while end for Employ evolutionary/reproduction stage Employ adaptation stage end while
```  
**Algorithm 1**Pseudocode of AAA  
Figure 2: Flowchart of AAA.  
The fitness function (FF) utilized in the presented AAA-FS system was planned to contain a balance between the amount of chosen features from all the solutions (minimal) and the classifier accuracy (maximal) reached by utilizing these chosen features; Equation (1) demonstrates the FF for evaluating the solution.  
\[\text{Fitness}=\alpha\gamma_{R}(D)+\beta\frac{|R|}{|C|} \tag{1}\]  
whereas \(\gamma_{R}(D)\) implies the classifier error rate of provided classier (the K-nearest neighbor (KNN) technique is utilized). \(|R|\) refers to the cardinality of the chosen subset and \(|C|\) signifies the entire amount of features from the dataset. \(\alpha\), and \(\beta\) are two parameters equivalent to the significance of classifier quality and subset length. \(\in[1,0]\) and \(\beta=1-\alpha\).
# Evaluation of Structural Behavior of Hysteretic Steel Dampers under Cyclic Loading
## 6 Conclusions  
This study experimentally and analytically evaluated the structural performance of the proposed D-shaped steel damper subjected to cyclic loading, and the following results were obtained.  
1. As a result of the experiment, SD1 and SD2 in which the vertical members were connected by semicircular and horizontal members, respectively, had similar load history characteristics, energy dissipation capacities, and equivalent damping ratios. Therefore, even if the shape of a  
Figure 11: Stress distribution of steel dampers obtained from FE analysis: (a) SD1; (b) SD2; (c) SD3.  
Figure 10: FE modeling of steel dampers used in this study: (a) SD1; (b) SD2; (c) SD3.  
connecting member is changed from the semicircular type to the relatively simple horizontal type, there will be no significant degradation in structural performance.
2. SD3, which added the horizontal member to U-shaped SD1, showed about 3.5 times more load-resisting capacity and about 3.8 times greater energy dissipation capacity than did SD1 in the experiment. In addition, SD3 had higher elastic stiffness and lower displacement at first yielding compared to those of SD1.
3. From the experiment, both SD2 and SD3 showed no decrease in stiffness and strength during five loading cycles, and they showed equivalent damping ratios of about 50%, confirming excellent damping capacity. In addition, SD2 and SD3 almost maintained their original shapes, even after five reversed cyclic loads, and no in-plane or out-of-plane buckling was found.
4. The FE analysis results for the energy dissipation zone of each damper were almost consistent with the experimental results. In the case of SD3, the yielding zone was concentrated in the vertical member and the lower horizontal member. Furthermore, the semicircular connecting member of SD3 showed similar stress distribution as SD1. This means the additional horizontal member is an important factor in determining the behavioral characteristics of the proposed D-shaped steel damper.  
**Author Contributions:** [PERSON]-W.K. and [PERSON]-H.K. conceived and performed the experiments and analyzed the data and wrote the paper. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by the Priority Research Centers Program through the National Research Foundation of Korea (NRF) founded by the Ministry of Education (No. 2019R1A6A1A03032988); This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) founded by the Ministry of Education (No. 2018R1A2B3001656).  
**Acknowledgments:** The authors would like to give sincere thanks to [PERSON] who is the president of HUB Structural Engineering, Co., Ltd. for supporting this work.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Results
### Factors Influencing Changes in Vegetation NDVI
#### 3.2.2 Relationship Between Precipitation and NDVI  
As shown in Figure 7, the annual precipitation in Aksu varied considerably, with the lowest level recorded at 72 mm in 2007 and the highest at 217 mm in 2010. NDVI also showed a positive correlation with precipitation, with a correlation coefficient of 0.39  
Figure 5: Annual average NDVI change trends in Aksu from 2000 to 2020.  
(R\({}^{2}\) = 0.1523). This weak correlation likely reflects the low and highly variable precipitation levels in Aksu, where annual rainfall is insufficient to sustain vegetation growth without supplementary irrigation. Furthermore, the region's vegetation primarily relies on groundwater and irrigation from rivers, rather than direct rainfall, as a water source. This decoupling between precipitation and vegetation growth reduces the strength of the observed relationship. Additionally, the spatiotemporal heterogeneity of precipitation patterns, coupled with anthropogenic interventions such as controlled water allocation for agriculture, further complicates the direct link between precipitation and NDVI.
## 5 Results and Discussion  
In this section, we detail the results of our experiments, demonstrating the advantages of the VRFormer model within the VRDeepSafety platform, particularly its integration of V2X communication for improved motion prediction, accident anticipation, and situational awareness. We provide a quantitative comparative analysis, showing VRFormer significantly outperforms the single-vehicle baseline (BEVerse-tiny) on all key performance metrics, including motion prediction, accident prediction, and 3D object detection, an outcome of its robust data fusion and multi-agent design. These findings are supported by quantitative data and detailed analyses.  
The evaluation results revealed significant gains in key performance areas. In motion prediction, VRFormer exhibited a 15% increase in accuracy (mIOU) compared with the baseline model, indicating an enhanced ability to anticipate vehicle trajectories. For accident prediction, VRFormer achieved a 12% increase in detection accuracy (APA) demonstrating its increased reliability in identifying potential dangers. In 3D object detection, a critical aspect of autonomous driving, VRFormer demonstrated a 17% improvement in detection accuracy (mAP), which is essential for a comprehensive understanding of the environment. These performance metrics show VRFormer's enhanced interpretation of and response todynamic driving conditions as a result of the training process performed in VRDeepSafety when compared to traditional single-vehicle approaches. The response time is decreased by 0.3 s.  
These results emphasize that VRFormer is superior to the single-vehicle model for managing dynamic driving conditions. This advantage comes from the ability to validate information and maintain better situational awareness through the integration of multi-source data. Through V2X communication, VRFormer overcomes the visibility problems caused by large vehicles by integrating inputs from sources and providing clearer views by combining data from different sources, including vehicles and infrastructure. This enables accurate and well-informed decision-making under difficult conditions, and the incorporation of multi-source data provides VRFormer operating within the VRDeepSafety environment, a broader understanding of the surroundings of the environment, and detection of changes in traffic patterns or pedestrians that may be missed by individual vehicle models. This ensures the safety and reliability of autonomous vehicles.  
As shown in Table 7, VRFormer demonstrates enhanced motion prediction accuracy, with a 15% improvement compared to the baseline single-vehicle model. VRFormer also achieved a 12% increase in APA for accident prediction, showing an improvement in identifying impending dangers, and a 17% increase in 3D object detection. In addition, our model reduced the response time by 0.3 s as well. These results clearly demonstrate the value of multi-agent data fusion to achieve better performance in real-time scenarios.
# Multi-UAV Cooperative Anti-Submarine Search Based on a Rule-Driven MAC Scheme
## 2 Materials and Methods
### Association Rule-Based Multi-Objective Utility Function  
The process of multi-UAV cooperative anti-submarine search is expected to complete the search task with the least fuel and the minimum search time and cost. Therefore, the utility functions consist of target discovery revenue, \(J_{p}\); execution cost, \(E_{c}\); collision cost, \(C_{ci}\); and threat avoidance cost, \(W_{c}\), under the following association rules.  
Target discovery revenue, \(J_{p}\), is defined as the possibility of the \(i\)-th UAV finding targets based on association rules:  
\[\begin{array}{ll}J_{pi}(k)=&\sum\limits_{(m,n)\in R_{i}^{2}}\left[(p_{d}-p_{ f})p_{mm}(k)+p_{f}\right]\\ &+\sum\limits_{s=1}^{s_{hp}}\left[\tau_{hp}(p_{d}-p_{f})p_{mm}(k)+p_{f}\right] \\ &+\sum\limits_{s=1}^{s_{hp}}\left[\tau_{hp}(p_{d}-p_{f})p_{mm}(k)+p_{f}\right] \end{array} \tag{17}\]  
Execution cost, \(E_{c}\), is defined as time and fuel consumed by the \(i\)-th UAV to fly to the next grid according to the path planned by rule \(S_{i}\):  
\[E_{ci}(k)=\mid\mid\mid\mid\mid\mid\mid\mid\mid\mid\mid\mid\mid\mid\mid\mid\mid \mid\mid\mid\mid\mid\mid\mid\mid\mid\mid\mid\mid\mid\mid\mid\mid\mid\mid\mid\mid\mid \mid\mid\mid\mid\ \ \ \ \ \  
each sub-group has an independent computing unit, which selects the route according to the designed state transition rules and interacts with other ants to deal with its local optimization problem. In the following sections, we will discuss pheromone initialization, state transition, and pheromone update and analyze how RI-MAC performs cooperative path optimization for multi-UAV cooperative search.