esa-sceva/satcom-chunk-collection huggingface dataset:
# Exploring Evolutionary Algorithms for Multi-Objective Optimization in Seismic Structural Design
## 2 Background
### Heuristics and Metaheuristics
The application of these algorithms is not limited to prediction; they also play a crucial role in the design and control of seismic response reduction systems. For example, a maximum energy dissipation algorithm (MEDA) and a modified variable energy dissipation algorithm (VEDA) control the modified energy dissipation algorithm using a magnetorheological (MR) damper, demonstrating the flexibility of metaheuristic algorithms in controlling semiactive devices for seismic response reduction [60]. The application of genetic algorithms to assess trade-offs between several objectives, such as inter-story drifts and total accelerations, is further demonstrated by the multi-objective evolutionary design of passive energy dissipation systems, offering insightful information to decision-makers [61]. Furthermore, the application of probabilistic seismic demand-based design algorithms to the rehabilitation of buildings equipped with hysteretic energy-dissipating devices (EDDs) demonstrates how these algorithms can be used in practice to guarantee structural integrity and safety after rehabilitation [62]. The combined findings of this research highlight the adaptability and efficiency of metaheuristic algorithms in handling several facets of seismic energy dissipation, ranging from design and rehabilitation to prediction and control.
# Reconfigurable Intelligent Surface Assisted Target Three-Dimensional Localization with 2-D Radar
## 4 Simulation Analysis  
The target localization algorithm is simulated with an RIS-assisted 2-D radar, and the localization accuracy is measured by the Geometric Dilution of Precision (GDOP) [32; 33], which is an index to measure the geometric accuracy of the localization system and represents the localization accuracy of the target in different directions. The smaller the target localization error, the lower the GDOP and the higher the localization accuracy. The fmincon function in the optimization toolbox of the MATLAB is used to solve the proposed convex optimization problem in (20).  
Firstly, a 2-D radar assisted by three RISs is simulated to indicate the feasibility and effectiveness of the localization algorithm, and the estimated GDOP versus the target SNR is simulated and compared with the derived CRLB in (38). Secondly, the localization GDOP and the CRLB of the target at different positions are covered. Thirdly, in the case of different RIS numbers and station layouts, the localization algorithm is simulated to demonstrate the influence of the RIS number and station layout on the GDOP. Fourthly, the localization results in a multi-target scenario are given. Finally, the influence of the site errors of the RISs is considered.
## 2 The Evolution and Trends of 6G Network Architecture
### Opportunities for Fundamental Change
#### 2.2.5 Human-Centric Networks
NSSF (Network Slice Selection Function): Using this function, the appropriate network slice is selected for the user based on service requirements, ensuring that the user is connected to the most suitable slice for their needs, such as enhanced mobile broadband or ultra-reliable low-latency communications. NEF (Network Exposure Function): This provides a standardized interface for external applications to interact with the network, enabling the exposure of network capabilities and services to third parties in a secure and controlled manner. NRF (Network Repository Function): This is a central repository that stores information about network functions and their capabilities, supporting service discovery and management within the network. PCF (Policy Control Function): This function enforces network policies related to user behavior, traffic management, and QoS, ensuring that the network resources are allocated in line with the operator's business and service rules. UDM (Unified Data Management): This is a centralized entity that manages subscriber data, including authentication, authorization, and accounting information, ensuring seamless mobility and service continuity for users across the network. AF (Application Function): This supports application-level services by interacting with network functions to manage application-specific behaviors such as data flow, security, and quality of service for specific applications or services. AUSF (Authentication Server Function): This function handles the authentication of users and devices in the network, ensuring that only authorized users and devices are granted access to network services. Figure 2: An overview of 6G architecture: a transition from 5G (adapted from [10]). It streamlines computing, storage, and networking, introduces sliced local breakout for low-latency inter-operator connectivity, adds an AI-plane alongside U-Plane and C-Plane, and disaggregates 3 GPP entities (PCF, AMF, and UPF) into cloud-based lambda functions. AMF (Access and Mobility Management Function): This function manages access to the network, handles mobility functions such as user equipment tracking and handovers between cells or base stations and ensures secure authentication of users. UPF (User Plane Function): This is responsible for managing user data traffic, including packet forwarding, routing, and applying policies related to data flow and quality of service.
## 4 Results and Analysis  
### Modal Frequency and Damping Ratio Identification  
The measured data from velocity sensor 1 in test 4 were analyzed. The time history of the signal and the concerned frequency spectrum are presented in Figure 11. In the  
Figure 10: Time history and spectra of test 1 to test 4.  
VMD algorithm, parameter \(K\) was set to 10 to allow for the consideration of more vibration modes and removing low- and high-frequency vibrations that are not structural vibration. Additionally, the convergence parameter \(\varepsilon\) was set to a value of \(10^{-8}\). Performing the VMD on the pure vibration signals after a low-pass filter with a cutoff frequency of 5 Hz, the MICs and corresponding spectrum are shown in Figure 12. The spectrum values for the four main frequencies of MICs are 2931, 8644, 1820, and 6633, as depicted in Figure 12, which are remarkably close to the values presented in Figure 11 (3053, 8639, 1845, and 6707), indicating that there is almost no energy leakage.  
Figure 11: Time history and spectrum of test 4.  
Figure 12: Time history and spectrum of derived components by the VMD for test 4.  
By applying the NExT method to each MIC, the free decay signals were obtained. Subsequently, the SDESA and HT methods were used to extract the amplitude envelope and time-varying frequency. The identified frequencies (\(f\)) and damping ratios (\(\xi\)) are listed in Table 3.
# Influence of Grit Size and Wood Species on the Granularity of Dust Particles during Sanding
###### Abstract  
Wood dust poses a threat to the health of employees and the risk of explosion and fire, accelerates the wear of machines, worsens the quality of processing, and requires large financial outlays for its removal. The aim of this study was to investigate the extent to which the grit size of sandpaper influences the size of the wood dust particles and the proportion of the finest particles which, when dispersed in the air, may constitute the respirable fraction. Six species of hardwood (beech, oak, ash, hornbeam, alder, and walnut), and three species of softwood (larch, pine, and spruce) were used in the research. While sanding the samples under the established laboratory conditions, the following were measured for two types of sandpapers (grit sizes P60 and P180): mean arithmetic particle size of dust and finest dust particles content (<10 \(\upmu\)m). Based on the obtained results, we found that the largest dust particle sizes were obtained for alder, pine, and spruce; the smallest size of dust particles during sanding with both sandpapers was obtained for beech, hornbeam, oak, ash, larch, and walnut. The mean arithmetic particle sizes ranged from 327.98 \(\upmu\)m for pine to 104.23 \(\upmu\)m for hornbeam. The mean particle size of the dust obtained with P60 granulation paper was 1.4 times larger than that of the dust obtained with P180 granulation sandpaper. The content of the finest dust particles ranged from 0.21% for pine (P60 sandpaper) to 12.58% for beech (P180 sandpaper).The type of wood (hardwood or softwood) has a significant influence on the particle size and the content of the finest dust fraction.  
wood dust; sanding; sandpaper; particle-size distribution +
Footnote †: journal: Applied Physics Letters
# Polarization Radiometric Calibration in Laboratory for a Channeled Spectropolarimeter
## 3 Method of Polarization Radiometric Calibration  
In the presented methods, we proposed a series of calibration methods of parameters in the polarization radiometric calibration model. We first briefly expound the classical radiometric calibration method to obtain the dark current response \(C(\sigma,\theta)\) and modulated radiometric calibration coefficient \(A^{k^{\prime}}(\sigma,\theta)\). Then the polarization parameters independent of field of view are calibrated with self-correction technique. Different from the radiometric calibration of traditional imaging spectrometer, the radiometric calibration coefficient and polarization parameters would be coupled in the calibration process for the fieldable CDISP. Therefore, we come up with a polarization radiometric decoupling calibration method to achieve the accurate calibration of the radiometric calibration coefficient \(A^{k}(\sigma,\theta)\) and diattenuation of fore-optics \(D_{fore}(\sigma,\theta)\). Finally, \(\exp[-i[\varphi_{1}(\sigma,\theta)+\varphi_{2}(\sigma)]]\) and \(\delta_{fore}(\sigma,\theta)\) are acquired by using the \(0^{\circ}\) and \(45^{\circ}\) linearly polarized reference beam.
## 2 Continuous Formulation of Topology Optimization Problem Statements and Sensitivities
### Topology Optimization Problem  
Consider the formulation of a generic topology optimization problem, defined as finding \(\rho\in L^{\infty}(\Omega)\) such that:  
\[\min_{\rho}:J(\rho,u(\rho)) \tag{1}\]  
subject to:  
\[\rho_{min}\leq\rho\leq 1 \tag{2}\]  
\[\int_{\Omega}\rho\;dx\leq V^{*} \tag{3}\]  
where \(\Omega\) is the domain of the topology optimization problem, \(L^{\infty}(\Omega)\) is the space of bounded functions, and the density \(\rho\) represents the design variables, which can vary within the interval \([\rho_{min},1]\). \(J\) represents a generic objective or cost function; \(J\)\(\rho\;dx\) represents a generic volume constraint function, where \(V^{*}\) is its maximum allowable value; \(u(\rho)\) are the displacement solutions of the standard equilibrium equation presented as finding \(u\in H^{1}_{0}(\Omega)\) such that:  
\[a(\rho,u,v)=l(\rho,v),\;\forall\;v\in H^{1}_{0}(\Omega) \tag{4}\]  
where \(a(\rho,u,v)\) is the constitutive function of the equilibrium equation, \(l(\rho,v)\) are the external forces, and \(H^{1}_{0}(\Omega)\) is the space of functions with square integrable derivatives and homogeneous values on the boundary and domain \(\Omega\in R^{D}\), with \(D\in[2,3]\). The bilinear form is:  
\[a(\rho,u,v)=\int_{\Omega}\bigtriangledown^{s}u\mathbb{C}(\rho)\bigtriangledown^ {s}v\;dx \tag{5}\]  
with the external forces \(l(\rho,v)\) written as:  
\[l(\rho,v)=\int_{\Omega}f(\rho)\;v\;dx+\int_{\partial\Omega}t\cdot nv\;ds. \tag{6}\]  
Here, \(\bigtriangledown^{s}\) represents a symmetric gradient and \(\mathbb{C}(\rho)\) is the constitutive tensor, and thus, the strains \(\xi(u)=\bigtriangledown^{s}u\) are in accordance with linear elasticity. \(v\) is the corresponding test function; \(f(\rho)\) is a generic volumetric load function; \(t\) indicates the applied boundary forces, if they exist; \(n\) is the normal direction pointing outwards of the boundary \(\partial\Omega\) of \(v\). It is important to note that this material constraint can be applied to either the volume or mass of the structure, following the same expressions. The volume constraint is preferred and used in this article to conform with the most-used term in the literature.  
When considering a maximum stress constraint, the problem statement is also subject to:  
\[\int_{\Omega}\sigma_{a}^{VM}(\rho,u(\rho))\;dx\leq\sigma^{*} \tag{7}\]  
where \(\int_{\Omega}\sigma_{a}^{VM}(\rho,u(\rho))\;dx\) represents the stress constraint with a maximum allowable value \(\sigma^{*}\). In particular, \(\sigma_{a}^{VM}\) is the penalized von Mises stress, as described in the following subsection.
## 3 Methodology
### Phase 1: Comprehensive Ablation Study Framework
#### 3.1.2 Ablation Component  
The ablation study systematically evaluates seven key architectural components in all selected models. The attention mechanism component involves complete removal of multi-head self-attention layers while maintaining the feedforward structure. Positional encoding ablation removes all position-aware information that forces models to rely solely on the token sequence without positional context. Feedforward network ablation eliminates fully connected position-wise layers while preserving attention mechanisms. Residual connection ablation removes skip connections, which requires models to learn representations through direct transformation paths. Layer normalization ablation removes all normalization layers and tests the model's ability to maintain stable gradients and representations. Ablation of regularization of dropout eliminates all dropout mechanisms to assess their contribution to generalization. In addition, model-specific components such as GELU activation functions, CLS token utilization, and enhanced attention mechanisms are systematically evaluated.
# An Event Extraction Approach Based on a Multi-Round Q&A Framework
## 3 Models
### Multi-Round Answer Extraction Model  
In this paper, two answer extraction models, a trigger detection model, and an argument element extraction model are proposed separately using BERT as the base model to obtain contextual representations from their input sequences, and the argument elements are updated during the training process. After instantiation using the question template, the sequence is shown in Equation (3):  
\[[CLS]<Question>[SEP]<Sentence>[SEP] \tag{3}\]  
The contextual representation of each token used for trigger detection and argument extraction is obtained using \(BERT_{Tt}\) and \(BERT_{Arg}\), respectively. For the input sequence \((a_{1},a_{2},\ldots a_{N})\) prepared for triggering detection, we have Equations (4) and (5):  
\[A=[a_{1},a_{2},\ldots a_{N}] \tag{4}\]  
Figure 3: Event extraction process based on multi-round Q&A framework.  
\[a_{1},a_{2},\ldots a_{N}=BERT_{Tr}(a_{1},a_{2},\ldots a_{N}) \tag{5}\]  
As for the input sequence \((b_{1},b_{2},\ldots b_{M})\) prepared for the argument element span extraction, we have Equations (6) and (7):  
\[B=[b_{1},b_{2},\ldots b_{M}] \tag{6}\]  
\[b_{1},b_{2},\ldots b_{M}=BERT_{Arg}(b_{1},b_{2},\ldots b_{M}) \tag{7}\]  
However the output layer of each QA model is different: the trigger detection model predicts the event type of each token in the sentence, while the argument extraction model predicts the start and end offsets of the argument span.  
For trigger prediction, this paper introduces a new parameter matrix \(W_{QAtr}\in R^{D\times H}\), where D is the hidden size of the transformer, H is the number of event types plus one (for non-trigger tokens), and the softmax normalization applies to H types, as shown in Equation (8):  
\[P_{QAtr}=softmax(EW_{QAtr})\in R^{D}\times N \tag{8}\]  
For the argument element prediction, two new parameter matrices \(W_{QAarg1}\in R^{D\times 1}\) and \(W_{QAarg2}\in R^{D\times 1}\) are introduced in this paper. Perform softmax normalization on the input tokens \(c_{1},c_{2},\ldots,c_{M}\)to obtain the probability of each token being selected as the start/end of the range of argument span, as shown in Equations (9) and (10):  
\[P_{QAarg1}(i)=softmax(c_{i}W_{QAarg1}) \tag{9}\]  
\[P_{QAarg2}(i)=softmax(c_{i}W_{QAarg2}) \tag{10}\]  
To train the model, the log-likelihood loss of both models is minimized in this paper. In particular, the loss of the argument extraction model is the sum of two components: the start token loss and the end token loss. For the training example with no parameter span (no answer case), we minimize the start and end probabilities of the first token of the sequence ([CLS]), as shown in Equation (11):  
\[\zeta_{QAarg}=\zeta_{arg\_start}+\zeta_{arg\_send} \tag{11}\]  
To obtain the type of each token \(a_{1},a_{2},\ldots,a_{N}\)at test time for trigger detection, simply apply argmax to \(P_{QAtr}\).
# Dual-Polarization Observations of Slowly Varying Solar Emissions from a Mobile X-Band Radar
## 6 Conclusions  
In conclusion, it is possible to state that the dual-pol receivers of the X-band radar were well calibrated during the 241-day period (2016) in Payeme, in both absolute and relative terms. An absolute calibration cannot, but consider noise-subtraction. As for the relative agreement, the radar was  
Figure 3: The same curves as in Figure 2, but after having subtracted the average value from each one.  
able to reproduce the slowly varying solar component: with 55 degrees of freedom, the correlation coefficient between sun+noise and the DRAO reference is 0.76 (0.67) for the vertical (horizontal) channel. The standard deviation of the difference is 0.18 (0.23) dB for the vertical (horizontal) channel. Our estimate of noise is not optimal; in fact, both the dispersion of the error and the correlation become worse after daily noise subtraction. For this reason, we prefer to use the original data to assess the relative error between H and V regardless of the DRAO reference. In this way, the horizontal channel has on average resulted to be +0.06 (\(\pm\)0.06) dB stronger than the vertical one. It is possible that, in the future, in order to improve the estimation of noise, \"Noise scan acquisitions\" could be attempted as suggested in the last part of Section 4.2 in [28].  
It could also be interesting to investigate the possible causes of the small drifts observed during the 241-day field experiment. Our hypothesis is that the temperature inside the Rx box could play a major role and in our future experiments we plan to measure the temperature inside the antenna-mounted receiver box in and investigate its correlation with the difference between the retrieved signal and the reference, as measured by the DRAO of the Canadian National Research Council.  
**Acknowledgments:** The authors would like to thank [PERSON] for the stimulating discussions regarding the measurements of the Sun using mobile X-band radars and [PERSON] for editing and checking Table A1. The valuable and helpful comments from anonymous reviewers are appreciated.  
**Author Contributions:** [PERSON] and [PERSON] conceived and designed the experiment; [PERSON] conceived and programmed the fully-automated, real-time, Sun-tracking scan program, which is run every 30 min and lasts less than 1 min; [PERSON] conceived the routines for the retrieval of Solar power and an estimate of background noise (in dBatu), starting from the raw radar spectra (expressed in ADU/Hz). [PERSON] analyzed the data and presented the results; after feedback and comments by [PERSON], [PERSON] wrote the paper.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 1 Introduction
The designed controller has been implemented using an Acorn RISC Machine-based LM4F120 microcontroller, and its performance has been examined experimentally on a WECS emulator prepared in the laboratory. It has been found to exhibit a better performance than the CSMC and P&O strategies in terms of the settling time and output voltages ripple. In [13], the authors have proposed a double integral SMC-based MPPT strategy for PMSG-WECS, where the reference signal (optimum DC-side current) has been generated through a fuzzy logic controller. The proposed strategy has been found to have more promising results than the CSMC and power signal feedback control strategies in terms of eliminating the generator-side current harmonics. The authors in [14] have proposed a voltage mode second-order SMC for improving the MPPT efficiency of a PMSG-WECS. In the proposed method, the second-order sliding surface has a proportional integral derivative (PID) type structure. The proposed technique has been found to exhibit a better performance than the CSMC. Advanced SMC strategies offering finite-time convergence, such as the conventional terminal sliding mode control (TSMC) and fast terminal sliding mode control (FTSMC), have been proposed to effectively minimize chattering. Both of these stated strategies utilize nonlinear sliding surfaces, where fractional power is introduced into the sliding surface to guarantee a fast as well as finite-time convergence during the sliding phase. The convergence speed of FTSMC is higher than the conventional TSMC [15]. However, just like the conventional SMC, the chattering phenomenon by TSMC and FTSMC is not eliminated completely. In [10], a novel fast dynamic terminal sliding mode control (FDTSMC) strategy, based on Lyapunov theory is proposed for a class of single-input and single-output uncertain nonlinear systems, for complete elimination of chattering. However, to the best of the authors understanding and knowledge, the FDTSMC-based paradigm has never been applied to accomplish the MPPT of a variable speed PMSG-WECS. In this article, a nonlinear MPPT control paradigm based on FDTSMC is proposed for a 3 kW, variable speed, fixed-pitch, PMSG-WECS-based standalone system. The proposed MPPT strategy converges the output tracking error to zero in a finite-time, realizes a high precision performance, offers fast dynamic response, reduces chattering to a minute level and guarantees global robustness. The performance of the proposed paradigm is tested and validated under a stochastic wind speed profile in Matlab/Simulink. A comparative analysis is performed among the proposed MPPT strategy, FTSMC, conventional sliding mode control (CSMC), feedback linearization control (FBLC) and proportional integral derivative (PID) control-based MPPT techniques, where the proposed MPPT strategy has been found superior to all the stated techniques in terms of the tracking performance.