esa-sceva/satcom-chunk-collection huggingface dataset:
## 8 Numerical Results and Discussion
### The SNR in the VLC Scenario with IRS Position Optimization Based on an RNN  
The SNR results in Figure 7 demonstrate the substantial benefits of incorporating IRS position optimization based on an RNN in VLC systems. Both pyramidal and hemispherical ADRs with MRC achieve a maximum SNR of 105.94 dB and a minimum of 69.65 dB,  
Figure 6: SNR distributions in the indoor VLC scenario without IRS position optimization for a single PD and different ADRs with signal selection or combination mechanisms.  
marking a significant improvement compared to their counterparts without IRS optimization (maxima of 74.86 dB and 76.00 dB, respectively). This enhancement highlights the ability of the RNN to optimize IRS positioning, effectively boosting received signal strength and ensuring greater coverage uniformity. In addition, the gap between the maximum and minimum SNRs is considerably reduced, indicating a more consistent signal quality throughout the room.  
For configurations using SBC, the RNN-optimized scenarios still deliver considerable improvements, with maximum SNR values reaching 91.28 dB and minima of 51.33 dB, compared to the non-optimized maximum of 60.78 dB and minimum of 38.62 dB. Although SBC consistently performs poorly relative to MRC, the use of RNN optimization narrows the gap, demonstrating its potential to improve less sophisticated signal processing methods. In general, these findings underscore the critical role of IRS optimization in improving the SNR distribution, especially when combined with advanced ADR geometries and processing mechanisms like MRC.
## Materials and Methods
### Assessment of Reproducibility Strength  
The assessment of the reproducibility strengths of proxies considered here will be primarily carried out by analyzing values of Lin's concordance correlation coefficient, symbolized by means of \(\rho_{C}\)[111]. Agreement will be defined as poor whenever \(\rho_{C}<0.90\), moderate for \(0.90\leq\ \rho_{C}<0.95\), good for \(0.95\leq\ \rho_{C}<0.99\), or excellent for \(\rho_{C}\geq 0.99\)[126]. Moreover, CCC reproducibility will be estimated by means of model performance metrics, such as the coefficient of determination (CD), standard error of estimate (SEE), mean prediction error (MPE) [127; 128; 129; 130]. Related formulae for these statistics are provided by Equations (A43) through (A49) in Appendix D. The Matlab and R codes involved in both fuzzy inference and convention statistical task are provided in the supplemental files section.
## 5 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Technique** & **Precision (\%)** & **Recall (\%)** & **mAP (\%)** & **Parameters** & **Average Test Time (s)** \\ \hline Res-CNN3 & 80.5\% & 80.9\% & 80.6\% & 51.7 M & 0.036 s \\ \hline ResNet-18 + TCCS + SS & 79.4\% & 80.3\% & 77.6\% & 11.4 M & 0.035 s \\ \hline ResNet-34 + TCCS + SS & 79.8\% & 80.1\% & 78.8\% & 21.8 M & 0.046 s \\ \hline ResNet-50 + TCCS + SS & 81.2\% & 81.6\% & 81.6\% & 25.6 M & 0.042 s \\ \hline ResNet-152 + TCCS + SS & 80.4\% & 81.7\% & 79.6\% & 60.2 M & 0.051 s \\ \hline \end{tabular}
\end{table} 
Table 3: Ablation experiment results. the defects category detection in terms of average testing time. Overall, the observation of the performance dynamics between Res-CNNN3 and ResNet-50 + TCCS + SS, clearly demonstrates the unique value of ablation studies in object detection training, testing, and analyses [29]. These studies certainly offer one the ability to carefully optimize and critically analyze the costs, benefits, and economics of each detection technique in order to recognize the most suitable technique based on application, implementation, and purpose.
## 5 Test of Mesh Independence  
In addition, four mesh sizes listed in Table 3 are compared with each other in order to investigate the relationship between mesh size and both calculation accuracy and calculation efficiency. 2304, 4320, 5184, and 6912 radiation faces are generated in the growth chamber for the four cases, respectively.  
Figure 4 shows profiles of magnetic potential and temperature along with radius. For \(A_{0r}\) and \(A_{0i}\) presented in Figure 4a,b, the results of case 2, 3 and 4 are in good agreement with each other, which illustrates that meshes for electromagnetic field are fine enough. As seen in Figure 4c, the similar temperature profiles are presented for case 1, 2 and 3, temperature profile of case 4 is slightly lower than those of case 1, 2 and 3. This is because more radiation surfaces reduce to bigger calculation error  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Case** & **Mesh Number** & **Number of Radiation Surface** & **CPU Time (s)** & **GPU Time (s)** & \(\Delta T\) (\&/cm) \\ \hline
1 & \(36\times 51\times 77\) & 2304 & 255 & 71 & 37.36 \\
2 & \(45\times 43\times 71\) & 4320 & 1971 & 97 & 34.88 \\
3 & \(56\times 43\times 71\) & 5184 & 3355 & 203 & 34.76 \\
4 & \(74\times 43\times 71\) & 6912 & 14,421 & 247 & 35.15 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Time consumed to solve AC = B by CPU and GPU with different mesh number.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**No.** & **Electromagnetic Field** & **Temperature Field** \\ \hline
1 & \(36\times 51\times 77\) & \(36\times 51\times 77\) \\
2 & \(45\times 64\times 131\) & \(45\times 43\times 71\) \\
3 & \(56\times 64\times 131\) & \(56\times 43\times 71\) \\
4 & \(74\times 64\times 131\) & \(74\times 43\times 71\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Mesh number for electromagnetic and temperature field (\(N_{\theta}\times Nr\times Nz\)).  
Based on the above analysis, 56 \(\times\) 64 \(\times\) 131 for electromagnetic field and 56 \(\times\) 43 \(\times\) 71 for temperature field are selected.
## 2 Methodology for the 3D Quality Assessment of UUSR
### Assessment Indicators  
#### 2.1.1 Indicators Selection  
Unlike traditional assessment indicators for large-scale areas, some GEFs may exhibit high similarity in small-scale areas, such as the earthquake intensity, groundwater type and pressure head, etc. However, to meet high-resolution demands, the geotechnical properties, groundwater quantity, and negative geological body are typically considered to vary both horizontally and vertically in areas covered by thick soil layers. Based on a comprehensive analysis of previous research findings [37; 38; 39], the first-level indexes of geotechnical parameter \(G\), hydrogeological parameter \(H\), and spatial distance to negative geological layers \(S\) are mainly considered. Furthermore, the secondary indexes are proposed based on the knowledge of five designers with extensive experience in shield tunnel design, as shown in Table 1.  
Figure 1: Framework of 3D quality assessment of UUSR based on GEFs.  
Geotechnical parameters \(G\) is one of the dominant parameters to design and construct a tunnel. Among the indicators, the intensity parameters (cohesion \(c\) and internal friction angle \(\varphi\)) and permeability parameter (hydraulic conductivity \(k\)) are selected to be the key indicators. The higher the intensity of the soil, which means a larger \(c\) and \(\varphi\), the easier it is to maintain tunnel stability. The lower the permeability of the soil, which means a smaller \(k\), the easier it is to prevent tunnel water gushing. So, under certain conditions of other factors, the greater the intensity of the soil, the lower the permeability, and the higher the UUSR quality.  
For hydrogeological parameter \(H\) in a specific construction area, the yield of single well (YSW) \(q\), which refers to the output ability of groundwater, is a significant parameter to design the dewatering scheme during tunnel construction. The larger the \(q\), the more difficult it is to dewater during the tunnel construction, and the lower the UUSR quality.  
In addition to the traditional parameters mentioned above, the spatial distance parameter \(S\) is also considered to have a strong influence on the difficulty degree of shield tunnel construction. The depth \(h\), and the distance \(ds\) and \(dg\) to the soft soil layer and gravel layer, which are the main negative geological layers in specific areas covered by thick soil layers, are proposed. The deeper and closer to the negative geological layers, the lower the UUSR quality.
## 6 Conclusions  
This paper presented a wideband fractional-N all-digital phase-locked loop (WBPLL) architecture that utilizes a triple-loop configuration to achieve frequency tuning from 1.9 to 6.1 GHz. The architecture employs the automatic frequency control (AFC) and counter-assisted phase-locked loop (CAPLL) in the first and second loops for coarse locking, while the third loop utilizes a digital sub-sampling architecture without a frequency divider for fine locking. Fractional-N frequency synthesis in the third loop is accomplished using a delta-sigma modulator (DSM) and a digital-to-time converter (DTC). The reuse of digital modules such as counters, comparators, and differentiators across the AFC and CAPLL loops significantly minimizes the area requirement. Moreover, the introduction of a moving average filter (MAF) effectively reduces the frequency overlap ratio of the digitally controlled oscillator (DCO) between the second and third loops, ensuring stable and reliable loop switching. The total power consumption of the WBPLL varies with the frequency range, consuming between 8.8 mW at the WBPLL minimum output frequency of 1.9 GHz and 12.8 mW at the WBPLL maximum output frequency of 6.1 GHz, all at a 1.0 V supply. Implemented in a 28 nm CMOS process, the WBPLL achieves an impressive area efficiency, occupying only 0.055 mm\({}^{2}\). The experimental results verify the robustness and precision of the WBPLL, demonstrating its suitability for high-performance applications. The phase noise performance and locking capabilities of the proposed architecture confirm its potential to support advanced communication systems such as 5G FR1 networks and IoT applications, making it a highly viable solution for modern frequency synthesis requirements.  
Conceptualization, B.K. and S.K.; methodology, Y.K.; writing--review and editing, H.S.; supervision, S.K. All authors have read and agreed to the published version of the manuscript.  
Figure 10: Phase noise graph of WBPLL linear phase\(-\)domain model.  
**Funding:** This research was supported in part by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (NRF-2021R1C1C1014592) and in part by the Korea Institute for Advancement of Technology (KIAT) grant funded by the Korea Government (MOTIE) (N0001415182419, The Competency Development Program for Industry Specialist).  
**Data Availability Statement:** Data are contained within the article.  
**Acknowledgments:** The EDA tool was supported by the IC Design Education Center (IDEC), Korea.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 7 Challenges and Opportunities
### FL Scalability  
The success of the FL process is heavily reliant on the involvement of local learners, who enable both global and local model changes. In this context, additional research is needed to create scalable FL algorithms at all levels, including determining the ideal number of UAVs and the frequency of local and global model updates. Furthermore, participants are presumed to be linked to the FL system at all times. However, specific UAVs in UAV networks may turn off, affecting learning performance due to energy or connection limitations. As a result, FL algorithms must withstand client dropouts by predicting such events.  
Another problem is finding the right UAV learners for the correct data, especially when the data is unlabeled or mislabeled. In reality, present research assumes that data is tagged, which is not necessarily the case with data collected by UAVs. As a result, the planned FL method should be resistant to such challenges, for example, by allowing the UAVs to learn the labels of acquired data as a first step.
## 2 Literature Review
### Automatic Mask Generation Model  
The automatic mask generation model is a specialized variant of SAM designed for fully automated mask generation, prioritizing segmentation quality over inference speed. This model operates in three stages. First, during the mask generation stage, a point grid is created by uniformly distributing points along the edges of the input image. If n points are placed along each edge, the full grid contains n\({}^{2}\) points. The image is then cropped based on user-defined scales, and masks are predicted in the enlarged regions. To refine these predictions, non-maximum suppression (NMS) is applied, eliminating overlapping masks and prioritizing those with higher intersection over union (IoU) scores. Second, in the filtering stage, masks exceeding a predefined IoU threshold are retained, while unstable masks or masks covering the entire image are removed. Finally, in the post-processing stage, connected components with an area smaller than 100 pixels are removed, and holes within masks smaller than 100 pixels are filled to enhance segmentation consistency.  
In this study, the automatic mask generation model was iteratively executed to detect objects in panoramic images of indoor construction sites. During each iteration, the point grid generation and threshold parameters were adjusted, and multiple filtering algorithms were developed to refine the predicted masks. For consistency, this study refers to the automatic mask generation model simply as \"SAM\" throughout the paper.
# An Interventional Surgical Robot Based on Multi-Data Detection
## 1 Introduction
However, due to the gap between the structures, the sensor reading will not be particularly accurate, and only the axial force can be detected, which cannot determine the deformation of the catheter head. The surgeon may misjudge the patient's blood vessels by not having enough sensing information, pushing the catheter forward and damaging the patient's blood vessels. Currently, catheters equipped with proximal audio sensors or micro-optical fiber sensors [10] can detect the force and deformation direction of catheter head force. Through multi-dimensional sensor data feedback, surgeons can strengthen their grasp of surgical conditions, so as to improve the safety of robotic surgery. However, the cost of this method is too high in practical application, including the cost of active components, cables and other consumables. Inspired by insect antennae, a mounting bracket for the catheter head is designed in this paper so that two half-bridge strain gauges can be attached to the catheter head. The strain gauge data and the pressure sensor data in the clamping device were combined to carry out the tube pushing experiment of different angles of the catheter through the blood vessel and collect the three-dimensional sensing data. LSTM network is used to predict and classify the collected data, so as to realize the function of recognizing the current angle through blood vessels. Compared with traditional force detection devices, this method can detect multi-dimensional sensing information and judge the current bending state through blood vessels. Giving the operator more data feedback greatly improves the safety of surgical robots. The remainder of this paper is structured as follows: In Section 2, the system structure of the interventional surgical robot and the working principle of the sensors are introduced. In Section 3, the experiment principle and procedure are given. In Section 4, network training and data analysis are carried out. Finally, our conclusions are given in Section 5.
## 7 Conclusions  
A novel algorithm has been presented, aimed at safely and optimally managing the routing of aircraft on an airport surface in future airport operations envisioned by the SESAR project. A reference scenario has been defined for taking into account the features of future airport operations that are relevant to aircraft taxi routing and planning. Target operational requirements are laid out for developing the routing algorithm. Constraints have been identified, and route optimality has been defined based on the minimization of the time the aircraft spends on the airport surface with engines on.  
The proposed algorithm first computes a standalone, shortest path solution from runway to apron or _vice versa_, depending on the aircraft being inbound or outbound, respectively. The presence of other aircraft on the airport surface is neglected at this stage. For taking into account the constraints due to other traffic on the airport surface, this solution is amended by a conflict detection and resolution task that attempts to reduce and possibly nullify the number of conflicts generated in the first phase. The conflict resolution makes use of the prioritization of aircraft in conflict and chooses the conflict resolution that allows for the minimum increase of the taxi time.  
An example application on a simple Italian airport exemplifies how the algorithm can be applied to true-world applications. Emphasis is given on how to model the airport surface as a weighted and directed graph with non-negative weights, as required for the input to the algorithm. A preliminary performance assessment is also presented on a hypothetical airport representing an Italian hub. Results suggest that the algorithm concept is capable of coping with heavy traffic levels. Future work will be concerned with evaluation of the algorithm's performances on the actual aircraft data of an Italian hub.
## 4 Discussion
Thus, the OSI1-reporter has a further advantage in that it shows a several-fold higher signal-to-noise ratio in comparison to the ARE reporter and has a higher dynamic resolution. In contrast to Keap1-Nrf2/ARE, the OSI1 reporter does not rely on slow translation events to accumulate the reporting transcription factor in the nucleus; therefore, its response is generally much faster, and the reaction to the stimulating substance can be followed easily in real-time. This enables the investigation of important kinetic parameters such as the uptake rate of the investigated substances or the bioavailability. The sensitivities of the ARE reporter and OSI1-reporter systems are similar for the tested substances and lie in the low \(\upmu\)M range in both cases. It should be emphasized, however, that the yeast system with the OSI1 reporter will mirror the Nrf2-Keap1/ARE system only with electrophilic oxidants that can attack trigger-cysteine in the respective sensor proteins. Substances that activate Nrf2 by non-oxidative mechanisms, such as the kavalactones and andrographolide, would not be expected to activate the OSI1 reporter, and indeed we show this to be so (Figure 3). Furthermore, because of differences in metabolism, yeast cells may, of course, be unable to modify some electrophilic compounds to convert them into active thiol oxidants as might happen in mammalian cells. Nevertheless, the OSI1 reporter system offers a quick, sensitive, economical alternative to the ARE reporter system for detecting physiologically active electrophilic oxidants and has the added bonus that it does not require animal sacrifice. Indeed, a recent report also drew attention to similarities between the sensing of electrophiles in fungi via Yap1p activation and the Keap1-Nrf2/ARE in mammals [49]. An added advantage of working with yeast is that having identified an active substance, knock-out libraries of non-essential yeast genes and libraries maintaining knock-outs of essential genes as heterozygous in diploid yeast lines are available for screens (www.euroscarf.de/index.php?name=News) to identify genes of interest related to the effects of the test substance [50]. **Author Contributions:** I.S. invented and established the OSI1 reporter system and performed initial experiments with allicin and other oxidants. A.U. performed the experiments with PEITC, AITC, SFM, MMF, DMF and kavalactones using the OSI1 reporter systems. [PERSON] performed all the experiments with the NRF2/ARE reporter system. [PERSON], [PERSON] and [PERSON] planned and supervised the experiments and wrote the manuscript. All authors have read and agreed to the published version of the manuscript. **Funding:** This research was funded by RWTH Aachen University (MCHG, A.J.S.) and Undergraduate Research Opportunity Program (UROP) of RWTH Aachen University (Grants to A.U. and I.S.). **Acknowledgments:** We thank [PERSON] for excellent technical assistance. **Conflicts of Interest:** The authors declare no conflict of interest.
## 4 Simulations on the Trapping System  
### Electrical Simulations  
The design of the trap electrodes was a result of a series of numerical simulations of the electric potential, used to optimize the trap components step-by-step. The simulations were based on the static problem approach, meaning that the varying electric fields were assumed to change slowly with respect to the electromagnetic wavelength. Within this approximation, the problem reduced to solving the Laplace equation, which was numerically treated in a script written in C language using the toolkit ROOT [26] and the package BEMSOLVER [27]. The latter is a library in which the algorithms required to solve electrostatic problems with complex charges distributions are already implemented and optimized. The approach of BEMSOLVER is based on the boundary element method (BEM), which converts a volume problem into a surface one. The basic idea behind this method is to consider only the surfaces of the objects, then to find a continuous electric charge distribution on these surfaces that satisfies the boundary conditions of the original problem. The numerical solution of the superficial charge density can be found by cutting the interfaces between different media in squared or triangular panels (mesh), small enough to consider uniform the charge density on the surfaces. At this point, the electric potential can be expressed as:  
\[\phi(\vec{r}\,)\,=\,\frac{1}{4\pi\epsilon_{0}}\,\sum_{i=0}^{N}\left(\int_{S_{i}} \frac{1}{|\vec{r}-\vec{r}\,^{\prime}|}\,dS^{\prime}\,\right)\sigma_{i} \tag{3}\]  
where \(\sigma_{i}\) are the i\({}^{\text{th}}\) superficial charge densities of the surfaces \(S_{i}\) and \(N\) is the panel number. Equation (3) can be reformulated in a compact way as \(A\cdot\vec{q}=\vec{p}\), where the \(N\times N\) matrix \(A\), which depends only on the mesh geometry, connects the vector \(\vec{q}\) containing the superficial charge distributions \(\sigma_{i}\) to the vector \(\vec{p}\) representing the applied voltage on the i\({}^{\text{th}}\) panel. BEMSOLVER employs two techniques for solving Equation (3). The first is the generalized minimum residual (GMRES) method [28], which aims at computing an approximate solution by running an iterative algorithm until a certain tolerance fixed by the user is reached. This method starts with an initial guess \(\vec{q}_{0}\) for the solution, then evaluates the first residual \(\vec{r}_{0}=\vec{p}-A\vec{q}_{0}\), on which the convergence of the algorithm is checked. If another iteration is needed, the new solution vector is computed as:  
\[\vec{q}_{i+1}\,=\,\sum_{j=0}^{i}\alpha_{j}\vec{q}_{j}+\beta\vec{r}_{i}\]  
to minimize the new residual \(\vec{r}_{i+1}\) by properly choosing the coefficients \(\alpha_{j}\) and \(\beta\). The second employed method is the fast multipole method (FMM) [29], which basically describes the potential in a certain point with the single contribution due to the nearest charge, whereas the effect of the remaining charges is evaluated as a perturbation given by multipole and local expansions.