esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Method Validation
#### 2.6.2 Limit of Detection (LOD) and Lower Limit of Quantification (LLOQ)  
The limit of detection (LOD) was defined as the lowest concentration that gave a signal-to-noise (S/N) ratio of a maximum 3/1. Serial dilutions 2, 5, 10, 20, and 40-fold of a zero blank authentic matrix sample containing endogenous AA were used for LOD determination based on S/N ratio measuring. The same approach was used for evaluation of low limit of quantification (LLOQ), defined as a minimal analyte concentration measured with acceptable accuracy and precision. According to the ICH guideline for instrumental methods, including chromatography, LLOQ is evaluated by S/N ratio 10/1 [19].
## 1 Introduction
For example, [PERSON] [28] proposed a weighted EI to make the balance between exploration and the exploitation more flexible; [PERSON] [29] proposed the EI matrix method to solve the multi-objective problem. Another famous AKDBO method is the LCB- based method [30]. The LCB function is an effective approach to balance exploration and exploitation by combining the predicted value and variance in a simple way [31]. Subsequently, a parameterized LCB (PLCB) method was proposed by using cool strategy to improve the ability to balance the exploration and exploitation of the original LCB [32]. [PERSON] et al. [33] considered the coefficient of variation of predicted values and variance to determine the weight factor adaptively during the sequential process. Further, some variants of the LCB methods focus on upper confidence bounding, such as, the Gaussian process upper confidence bounding (GPUCB) algorithm proposed by [PERSON] et al., which considers the upper confidence bound of noisy functions [34]. The parallel type of the GPUCB was developed by [PERSON] et al. [35]. It mentions that LCB methods have been widely applied to solve real engineering problems [36; 37]. However, the weight factor for balancing the exploration and the exploitation of the LCB based method remains an interesting problem. This is because most of the existing factor approaches are subjective or problem dependent, which is not robust in application for all cases. In this paper, an entropy weight-based lower confidence bounding approach (EW-LCB) is proposed to ascertain the weight of the LCB function adaptively and objectively. In the proposed EW-LCB method, entropy theory is used to quantify the degree of variation of the predicted value and variance of the Kriging model. Then, a new weighted formula is introduced to allocate the weights of exploration and exploitation adaptively. To validate the performance of the proposed EW- LCB method, several numerical functions with different dimensions and complexities and an engineering problem are tested. The computational efficiency, accuracy of the optimum, and the robustness are considered when comparing EW-LCB with the existing famous AKBDO methods. Results showed that the performances of the proposed EW-LCB approach were competitive on the test cases. The remainder of this paper is organized as follows, in Section 2, the basis of the Kriging model and several existing famous AKBDO methods are introduced. The details of the proposed approach with the assistance of an illustrative example are described in Section 3. In Section 4, the effectiveness of the proposed approach is tested on several numerical benchmark problems and an engineering design optimization problem. Finally, some conclusions and possible future works are proupposed in Section 5.
## 1 Introduction
This study parallels the experimental report in [21], where we achieved a bit rate of 38 Gbps using optimised OFDM with bit and power loading. However, the goal of this work is to demonstrate the rates achievable using simple PAM modulation schemes and to experimentally show the effectiveness of NNs in compensating for non-linearity and ISI. While [21] aims to maximise the achievable bit rate through an optimised OFDM scheme, this work focuses on the non-linear behaviour of VCSELs and explores possible mitigation using an NN-based non-linear equaliser with multilevel PAM. Moreover, we employ lower modulation amplitudes in this study, as PAM has a lower peak-to-average-power ratio than OFDM.
# A Linear Strong Constraint Joint Solution Method Based on Angle Information Enhancement
## 5 Experimental Performance Verification
### Time Frequency Synchronization Performance Verification
This section adopts a multi-window joint estimation mechanism to accumulate group peaks, which not only improves synchronization accuracy, but also enhances the robustness of coarse synchronization, providing accurate and stable initial reference points for frequency offset estimation and fine timing synchronization, and improving time-frequency synchronization performance. After performing coarse timing synchronization, it is necessary to estimate and compensate for the frequency offset. In the simulation, the normalized frequency offset is set to 0.25, and a CP-based method is used for frequency offset estimation. The SNR and RMSE of the normalized frequency offset are shown in the following Figure 7. The method proposed in this chapter has good timing coarse synchronization performance, which further affects frequency offset estimation and improves the accuracy of CFO estimation. (2) Fine timing synchronization simulation and analysis  
After estimating the CFO, the frequency is compensated to obtain corrected frequency information, and a fine timing synchronization method based on the PSS sliding cross-correlation is used. The CP-based timing synchronization method cannot achieve fine synchronization due to the short length and poor correlation of the CP sequence. Therefore, this section compares the PSS sliding cross-correlation synchronization method with the three-level time-frequency synchronization method based on group peak timing tracing  
Figure 6: Performance comparison of coarse synchronization algorithms. proposed in this chapter, as shown in the following Figure 8. The PSS sliding cross-correlation synchronization method can achieve accurate synchronization accuracy, but due to its lack of frequency offset elimination, its timing synchronization accuracy is slightly lower than the algorithm proposed in this chapter. At high signal-to-noise ratios, the algorithm proposed in this chapter can synchronize to sampling accuracy. Even at low signal-to-noise ratios, the algorithm can achieve accurate synchronization accuracy and has precise and robust synchronization performance. The PSS-based correlation operation can not only perform timing synchronization, but also search for sectors. The search results are shown in the following Figure 9. The Cell ID of this simulation is 16, corresponding to \(N_{ID}^{(2)}=1\). When using this sequence for correlation operation, obvious peaks can be found. Figure 4:0 Fine timing synchronization. Figure 4:1 CFO estimation error.
## 2 Materials and Methods
### The Methodology  
The proposed methodology consists of two parts, i.e., offline training and online condition monitoring, depicted in Figures 1 and 2, respectively. The offline training includes the typical diagnostic process steps, i.e., data collection, feature extraction, feature selection, model selection, and model training for fault classification [2]. This phase aims to find the best feature subset and the best models for fault detection and diagnosis (novelty detection). The online condition monitoring aims to assess the current condition of the monitored system. Hence, based on the output of the training phase (green squares in Figure 1), it evaluates if the extracted feature vector from a signal segment of a defined length can be classified into the nominal class, into one of the known faults, or corresponds to a novel behavior.  
The methodology was coded in Python using several libraries, e.g., Numpy, Pandas, SciPy, and Sci-Kit learn. For models training and testing, an Intel(r) CoreTM i7- CPU @ 2.8 GHz and 16.0 GB RAM workstation has been used.  
The following subsections describe each part of the methodology.
## 3 Results and Discussion
### Antibacterial Activity  
Pytochemical constituents present in plants, serve as a defensive tool against microorganisms, insects, and herbivores [48; 49]. The demonstration of antibacterial activity against bacterial strains may indicate the presence of antibiotic compounds. For antibacterial activity, there might be two of these mechanisms. Firstly, due to reduced adenosine triphosphate (ATP) synthase activities, GNPs might have changed the membrane potential. In the second mechanism, GNPs might have collapsed the biological mechanism by decreasing levels of subunit of the ribosome for tRNA [28]. Synthesized gold nanoparticles showed effective antibacterial activity against the test pathogens as compared to ethyl acetate extract of _Quercus incana_. Nanoparticles showed inhibition at the concentration where ethyl acetate was unable to inhibit test pathogens. GNPs were most effective against _Bacillus subtilis_ and _Salmonella setubal_, where they showed 9 and 7 mm zones of inhibition at the concentration of 75 \(\upmu\)g (Table 3). They were least active against _Pseudomonas pickettti_ and _S. aureus_, showing 3 and 6 mm zones of inhibition at the concentration of 75 \(\upmu\)g, but inactive at 25 \(\upmu\)g. Here, it was also found that with an increase in the concentration of GNPs, the zones of inhibition also increased, even exceeding the antibiotic's zone of inhibition.
## 1 Introduction  
Compared to traditional cameras, light field cameras not only capture the cumulative intensity of light rays from all directions at spatial points but also record the intensity of light rays in different directions. Therefore, by capturing a single image, multi-view scene information can be obtained [1, 2, 3, 4], and this information can be used to precisely recover the three-dimensional structure and depth information of the scene [5, 6, 7, 8, 9], providing new data sources for subsequent image processing, pose estimation, and three-dimensional reconstruction [10, 11, 12, 13], directly affecting the accuracy and authenticity of the final model.  
Due to the internal structure of the light field camera and the particularity of light field data, the accuracy of existing depth estimation algorithms is poor [14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27], especially because the fixed neighborhood selection range cannot dynamically utilize visual featuresand the microlens array destroys the target features, which reduces the texture richness and causes the mismatch problem.  
Addressing the limited neighborhood selection and mismatching of weak texture regions, we propose a light field depth estimation algorithm that considers blur features and prior knowledge of planar geometric structures. Our contributions are twofold:  
1. We devise a multi-constraint approach that integrates photometric consistency, blur features, and geometric consistency to adaptively pick neighborhood micro-image sets with varying degrees of blur. This approach ensures that the selected sets are optimal for depth estimation purposes.
2. We propose a matching algorithm grounded in the prior knowledge of planar geometric structures. Initially, high-reliability depth information is acquired as the starting point. Concurrently, plane priors are introduced to facilitate the construction of multi-view aggregation matching costs and optimize the matching costs for pixels located in texture-weak regions. Ultimately, mismatched points are eliminated by leveraging the polar geometric relationships among microimages.  
In order to fully present our work, the paper is structured as follows: The second part is dedicated to Related Work, where the latest research on depth estimation is introduced. The third part presents the Methodology of this paper, providing a comprehensive description of the specific implementation of our approach. The fourth part outlines the Experimental Verification, detailing the specific content of our experiments. Finally, the fifth part presents the Conclusion and Future Work of this paper.
# Finite Element Analysis of Thermal Stress and Thermal Deformation in Typical Part during SLM
## 2 Mobile Heat Source and Thermal-Mechanical Coupling Algorithm
### Heat-Transfer Equation  
According to the definition of the heat flux density per unit cross-sectional area of the object,  
\[q_{r}=k\frac{\Delta T}{d_{r}} \tag{4}\]  
where \(k\) is the thermal conductivity (W/m K); \(\Delta T\) is the temperature difference from high to low (K); \(d_{r}\) is the thickness of the material in the current layer (m). When Equation (4) is combined with Equation (3), the temperature difference can be used to calculate:  
\[\Delta T=\frac{2 APd_{r}}{\pi\omega^{2}k}\exp(-\frac{2r^{2}}{\omega^{2}}). \tag{5}\]  
Furthermore, the heat transfer equation is  
\[\rho c_{p}\frac{\partial T}{\partial t}=\
abla(-k\Delta T) \tag{6}\]  
where \(\rho\) is the material density (kg/m\({}^{3}\)), \(c\) is the specific heat capacity (J/kg K), and \(t\) is the interaction time.  
The porosity of the powder can be calculated as  
\[\phi=\frac{\rho_{bulk}-\rho_{power}}{\rho_{bulk}}. \tag{7}\]  
Consequently,  
\[\rho_{power}=(1-\phi)\rho_{bulk} \tag{8}\]  
\[k_{power}=(1-\phi)k_{bulk}. \tag{9}\]  
The thermal equilibrium equation satisfies the following classical 3D heat conduction equation, given by [17]  
\[\rho c\frac{\partial T}{\partial t}=\frac{\partial}{\partial x}(k\frac{ \partial T}{\partial x})+\frac{\partial}{\partial y}(k\frac{\partial T}{ \partial y})+\frac{\partial}{\partial z}(k\frac{\partial T}{\partial z})+Q \tag{10}\]  
where \(Q=q^{*}(s^{*}t)\) 1 (\(x\), \(y\), \(z\), \(t\)) is the volumetric heat generation (W/m\({}^{3}\)).
# Multimodal Network for Object Detection Using Channel Adjustment and Multi-Scale Attention
## 3 Methodology  
### Architecture Overview  
The architecture of our proposed model is shown in Figure 1. It includes a dual-stream feature extraction backbone, a two-unit feature enhancement and fusion module, and an FPN module with a detection head. Infrared (IR) and visible (RGB) images are first processed independently through parallel ResNet-50 [28] networks, where each stream generates five hierarchical feature maps corresponding to different stages of the network. At each stage, five DCA-MAAM modules are applied to fuse feature maps. The fusion process begins with a dynamic channel adjustment (DCA) strategy, which adaptively recalibrates cross-modal feature channels by learning modality-specific importance weights. Subsequently, the Multi-scale Activated Attention Mechanism (MAAM) activates both local and global contextual information through parallel convolutional branches with varying receptive fields, enhancing the model's ability to capture objects at different scales. The fused features are processed by a Feature Pyramid Network (FPN) [29] to build a multi-scale feature hierarchy, which is then fed into YOLOX detection heads for final predictions. The detection heads output bounding box coordinates, objectness scores, and class probabilities, enabling robust multimodal detection. The framework is optimized using a combined loss function that integrates classification, localization, and spatial alignment objectives.
## 2 Problem Statement and Related Work
The use of small ensemble sizes in the other hand may result in undersampling the covariance matrix (the considered ensemble is not representative of our systems dynamics) which may in turn result in poor reconstruction performance, including for instance filter divergence and spurious long-range correlations. Proposed solutions such as inflation [32], cross-validation [33] and localization methods [34; 35; 36] may require thorough tuning experiments. An alternative strategy based on a model-driven propagation of parametric covariance models [23; 24] seems appealing. Using advection priors [37], it propagates parametric covariance structures, which leads to the implementation of the classic Kalman recursion. Accounting for more complex dynamical priors for the covariance structure is an open question, which may limit the applicability of this approach to complex geophysical systems. Inspired by the latter parametric framework, we aim to design an efficient sequential filtering technique for the reconstruction of geophysical fields. Rather than considering a model-driven prior to propagate Gaussian states as in [23; 24], we investigate NN-based priors, which may be fitted from training data. The resulting NN-based Gaussian representations provide computationally-efficient approximations of the dynamical priors that should prevent undersampling issues within a Kalman recursion.
# An Algorithm to Find the Shortest Path through Obstacles of Arbitrary Shapes and Positions in 2D
## 3 Materials and Methods
### Adapting the A* Algorithm for Such Graphs
#### 3.5.3 Expanding a Node on an Expanded Obstacle  
Before describing this process, we prove a general theorem that brings an appreciable simplification to the application of the A* algorithm. Namely, it allows a disregard for the step that consists in remarking as open a node that was already marked as closed. We note that this theorem is valid for any graph in which the triangle inequality holds.  
**First-Node Theorem.**_Once a node \(n_{i}\) has been expanded, it will never thereafter appear in a path from \(S\) that is shorter than its path from \(S\) at the time it was expanded._  
Proof.: Figure 10 represents the geometrical situation that corresponds to this theorem. In this drawing, the dashed lines represent paths that link two nodes in the graph, and the full lines represent rectilinear paths between two nodes. According to the A* algorithm, when a node \(n_{i}\) is expanded it is, at that time, the node with the smallest value of \(f\). Thus, for every other node \(n\), which had already been generated by A* and was still open,  
\[f(n)\geq f(n_{i}) \tag{2}\]  
If at some later time during the execution of the algorithm, the node \(n_{i}\) appears as a successor of \(n\), then on the new path through \(n\), \(\{S\), \(n\), \(n_{i}\}\) is such that:  
\[f_{new}(n_{i})=g(S,n)+g(n,n_{i})+EDist(n_{i},T)\]  
but because of the triangle inequality  
\[g(n,n_{i})+EDist(n_{i},T)\geq EDist(n,T)\]  
such that \[f_{new}(n_{i})\geq g(S,n)+EDist(n,T)=f(n)\] (3)  
Figure 9: Result of the expansion of an end node (\(n_{5}\)).  
then, from Equations (2) and (3),  
\[f_{new}(n_{i})\geq f(n_{i})\ \ \ \text{and therefore}\ \ \ g_{new}(S,n_{i})\geq g(S,n_{i}).\]  
Thus, any path to \(n_{i}\) through \(n\) is necessarily not longer than the initial path to \(n_{i}\), and could therefore be discarded from further processing in the shortest path construction. \(\square\)  
We now describe the operations involved in the node expansion. Figure 11 illustrates the situation in which an end node \(x=n_{2}\) on the obstacle +\(O\), which is shown surrounded by a small circle, has to be expanded. The bare black dots are end nodes, the black dots surrounded by a small square are already expanded nodes, and the white dots represent the points of departure of some free bitangents. We denote by \(n\) the closest expanded node that precedes \(x\) on the obstacle boundary; this would be node \(n_{5}\) in Figure 11. The _IntegrateExpandedNode_ procedure, described hereafter, is then performed to expand node \(x\).
## 2 Overview
### Marchetti Dilatometer: Conceptualization
\begin{table}
\begin{tabular}{c c c c c} \hline \(K_{o}\) & [-] & Coeff. Earth Pressure & \(K_{o,\,DMT}=(K_{D}/1.5)^{0.47}-0.6\) & For \(I_{D}<1.2\) \\ \hline \(OCR\) & [-] & Overconsolidation Ratio & \(OCR_{DMT}=(0.5*K_{D})^{1.56}\) & For \(I_{D}<1.2\) \\ \hline \(C_{u}\) & [kPa] & Undrained Shear Strength & \(C_{u,\,DMT}=0.22*e^{\prime}{}_{vo}*(0.5*K_{D})^{1.25}\) & For \(I_{D}<1.2\) \\ \hline \(\varnothing\) & [\({}^{\circ}\)] & Friction Angle & \(\varnothing_{safe,\,DMT}=28^{\circ}+14.6^{\circ}*\log(K_{D})-2.1^{\circ}\log^{2 }(K_{D})\) & For \(I_{D}>1.8\) \\ \hline \(C_{h}\) & [cm\({}^{2}\)/min] & Coefficient of consolidation & \(C_{h,\,DMT}\approx 7\) cm\({}^{2}/t_{flex}\) &
\begin{tabular}{c} t\({}_{flex}\) from along A-log t \\ DMT-A decay curve \\ \end{tabular} \\ \hline \(k_{h}\) & [cm/min] & Coefficient of permeability & \(k_{h}=C_{h}*\gamma_{w}/M_{h}\) ; \(M_{h}\approx K_{o}*M_{DMT}\) & - \\ \hline \(\gamma\) & [kN/m\({}^{3}\)] & Unit Weight and Description & (See Figure 5, considering \(\gamma_{w}=9.81\) kN/m\({}^{3}\)). & - \\ \hline \(M\) & & \(M_{DMT}=R_{M}*E_{D}\) & \\  & & & If \(I_{D}\leq 0.6\)\(R_{M}=0.14+2.36*logK_{D}\) & \\  & & & If \(I_{D}\geq 3\)\(R_{M}=0.5+2*logK_{D}\) & \\ \(M\) & [MPa] & Vertical Drained & If \(0.6<I_{D}<3\) & - \\  & Constrained Modulus & \(R_{M,0}=R_{M,0}+(2.5-R_{M,0})*logK_{D}\) & \\  & & & \(R_{M,0}=0.14+0.15*(I_{D}-0.6)\) & \\  & & & If \(K_{D}>10\)\(R_{M}=0.32+2.18*logK_{D}\) & \\  & & & if \(R_{M}<0.85\), set \(R_{M}=0.85\) & \\ \hline \end{tabular}
\end{table} 
Table 2: Basic DMT reduction formulae, [PERSON] (2001) [2]. Figure 4: Basic principles of Marchetti Dilatometer: pushing, contact stress A, expansion stress B, pressure C, adapted from [4]. To define the type of material, which can be inorganic or organic silt, a verification of LL was done. It consisted of determining the LL in several blows of 25 \(\pm\) 1 with oven-dried material. If the ratio of the LL\({}_{\text{oven dried}}\), with respect to the LL\({}_{\text{not dried}}\), is lower than 0.75, it was classified as organic [19]. Furthermore, to show how the behaviors of the LL and IP are with respect to deep, Figure 7 is presented below.