esa-sceva/satcom-chunk-collection huggingface dataset:
# Correction of Thermal Errors in Machine Tools by a Hybrid Model Approach
## 1 Introduction and State of the Art  
In machine tools, thermo-elastic structural deformations cause up to 75% of the machining inaccuracies measurable on the workpiece [1, 2]. Hereby, thermal errors in machine tools are considered to be much more relevant than geometric, static, and dynamic errors [3, 4]. Nevertheless, the correction of thermally induced errors in the development and operation of machine tools is currently regarded as insufficiently solved and a recognized problem in industry [4]. Established procedures are specific to machine, load case, and environmental boundary conditions [5]. This is a particular issue for Small- and Medium-sized Enterprises (SMEs), as they usually have small batch sizes, a wide range of parts, and heterogeneous machinery as well as usually no air conditioning for machine tools available [6]. Whereas large companies, as series producers, regard thermal-related faults in operation as dominant and worthy of correction, small- and medium-sized companies regard the problem of thermal behavior as too complex to tackle. Time- and energy-inefficient warming-up phases or complicated temperature control strategies produce significant costs, along with potential rejects. During the summer months, temperature fluctuations of 5 to 15 Kelvin during the day occur, which increases the need for a suitable correction procedure [7, 8, 9]. Consequently, the performance of the machine tools falls short of what is theoretically possible [10].  
The available approaches are divided into compensation and correction methods, where the first aim to minimize or prevent the occurrence of thermally induced deformations by influencing heat inputs and heat flows, and the latter aim to correct the thermally induced displacements at the tool center point (TCP) by adapting the movement specifications [11; 12]. Examples for compensation methods are thermo-symmetric structure design, temperature control of drives, bearings and guides, frame components, as well as temperature control of the cooling lubricant, partly by evaluation of the ambient temperature based on a reference sensor as well as air conditioning of the environment and air cooling [13; 14; 15; 16]. However, compensation methods are often expensive and do not include internal heat sources. In addition to other approaches, for example, using learning algorithms [17; 18; 19; 20; 21; 22; 23], the main approaches for correction methods are characteristic diagram-based correction and structure model-based correction, as outlined next.
# Rapid In Situ Biomonitoring of Subsoil Contamination by Applying an Algae-Soaked Disc Seeding Assay
## 4 Conclusions  
This study applied the algae-soaked disc seeding assay to consider periodic evaluation of subsoil contamination under different contamination configurations of silver nanoparticle soils via multiple toxicity endpoints using the green alga _C. reinhardtii_. It enabled us to observe that the fourth soil layer-treated group showed initial significant effects faster than other exposure scenarios and the third, second, and first soil layer-treated groups showed initial significant effects in that order. A significant increase in cell size, cell granularity, oxidative stress, and membrane permeability and a decrease in esterase activity were observed in all exposure scenarios at the end of the experiment. These results may have been caused when the _C. reinhardtii_ adhered to nanoparticles in the topsoil in the fourth soil layer-treated group, and silver ions dissolved from the NPs in the third, second, and first soil layer-treated groups leached into the soil. Therefore, the algae-soaked disc seeding assays can sense a change of subsoil contamination by periodic evaluation over time, because they were influenced by ionized pollutants that leached from the NPs distributed in subsoil. Overall, the results showed that the algae-soaked disc seeding assay is a useful method to evaluate the periodically rapid in situ toxicity of soil pollutants as a biomonitoring technique to measure subsoil contamination. However, further investigation is needed to study the use of various soil algae and soil textures and consider the toxicity mechanism and fate of soil pollutants in these cells and the soil matrix.  
Conceptualization, S.-H.N. and Y.-J.A.; methodology, S.-H.N. and J.I.K.; formal analysis, S.-H.N. and J.I.K.; investigation, S.-H.N. and J.I.K.; data curation, S.-H.N.; writing--original draft preparation, S.-H.N. and J.I.K.; writing--review and editing, Y.-J.A.; supervision, Y.-J.A.; funding acquisition, Y.-J.-A. All authors have read and agreed to the published version of the manuscript. This research was funded by Basic Science Research Program through the National Research Foundation (NRF) of Korea funded by the Ministry of Science, ICT and future planning (2020R1A2B58B02001734). This paper was also supported by Konkuk University Research Fund in 2020.  
Not applicable.  
Data Availability StatementData is contained within the article.  
We thank the Korea Basic Science Institute (KBSI) for the FE-SEM, EDX, and surface area analyzer analyses.  
The authors declare no conflict of interest.
# Optimization of Sensor Placement for Modal Testing Using Machine Learning
###### Abstract
However, this approach requires multiple experimental trials of sensor configurations, which is a costly approach for complex and large systems such as aerospace structures. Similarly, [PERSON] et al. [12] employed a data-driven approach for optimal sensor placement of a multi-zone building. Their method requires that experiments with a large number of sensors are initially completed to collect the data necessary to apply the sensor placement approach. These data-driven methods often rely on operational modal analysis to extract the modes of the system during operations. [PERSON] et al. [13] applied data-driven sensor placement to the observation of fluid flows. To the authors' knowledge, no data-driven methodologies have been proposed for modal analysis to date. This paper proposes a non-iterative model-based approach for optimal sensor placement in modal testing. Finite element models are generally available for large aerospace structures and can, therefore, be used to identify the number and positioning of sensors in the structure. The approach is based on machine learning techniques to avoid the need for iteration. Machine learning (ML) techniques are a promising approach for determining sensor placement in modal analysis. In supervised machine learning, an input dataset is provided consisting of both the input data and the output. In this case, the input would be data derived from a finite element model and the output would be the mode shapes and natural frequencies. Based on this information, the model is then trained to be able to predict outputs based on new input data. As many of the previously discussed methods for sensor placement are iterative approaches, the problem of solving for sensor placement seems to be one to which machine learning is well suited [14; 15; 16; 17]. This paper presents a novel methodology for sensor placement in modal analysis using random forest techniques. An initial application of the proposed approach is discussed in [PERSON] et al. [18]. The paper is organized as follows. First, a review of traditional methodologies currently used for sensor placement in modal analysis is presented. Then, the proposed machine learning approach is presented and validated for a vibrating beam. The results of the proposed approach are then compared with the results obtained using one of the traditional methodologies.
## 3 Parametric Analysis
### Parameters Characterizing the Frame Structures Used in the Analyses  
In the parametric analyses, two different multi-story frame structures were utilized, and their main characteristics are presented in Table 1. In each multi-story frame structure, the mass, stiffness, and height of the stories were assumed to be constant at every level. The values in the last column, Main Period, represent the periods corresponding to the first vibration mode of the reference structures, which are modeled as multi-degree-of-freedom shear-type frame structures. This information was included as it constituted one of the essential inputs required for applying the dynamic equivalence criterion proposed in [4].  
By applying such a dynamic equivalence criterion to the frame structures in Table 1, the characteristics of the corresponding 2-DOF models used in the analyses shown in Table 2 were derived.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Stories** & **Floor Area (m2)** & **Story Mass \(m_{s}\) (kg)** & **Story Height (m)** & **Main Period (s)** \\ \hline Frame 1 & 4 & 300 & 361.8 \(\times\) 10\({}^{3}\) & 3 & 0.48 \\ Frame 2 & 3 & 200 & 241.2 \(\times\) 10\({}^{3}\) & 3 & 0.39 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Geometrical and mechanical characteristics of the two reference frame structures.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Stories** & **\(k_{1}\) (Nm\({}^{-1}\))** & **\(k_{2}\) (Nm\({}^{-1}\))** & **\(m_{1}\) (kg)** & **\(m_{2}\) (kg)** \\ \hline Frame 1 & 4 & 7.933 \(\times\) 10\({}^{8}\) & 2.441 \(\times\) 10\({}^{8}\) & 361.8 \(\times\) 10\({}^{3}\) & 1085.4 \(\times\) 10\({}^{3}\) \\ Frame 2 & 3 & 4.384 \(\times\) 10\({}^{8}\) & 1.879 \(\times\) 10\({}^{8}\) & 241.2 \(\times\) 10\({}^{3}\) & 482.4 \(\times\) 10\({}^{3}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Mechanical characteristics of the 2-DOF equivalent models.
## 3 Methods
### Prune Method for Redundancy of Point Clouds
To decide the threshold \(T\), which is a value in the collection of all points' coordinate displacement \(|\Deltacoords_{t}|\) at \(t\)-timeslot, we should first sort the \(|\Delta Coords_{t}|\) in ascending order and mark it as \(|\Delta Coords_{t}|_{sorted}\). Subsequently, we establish a pruning ratio(\(ratio\)) to denote the proportion of points that should be considered background points. Then, we multiply the pruning ratio with the length of \(|\Delta Coords_{t}|_{sorted}\) (\(t\)) to find the index(\(target\)) of the last background point, and the last background point's value is set as the threshold \(T\). The index \(target\) is defined as follows. \[target=ratio\times l. \tag{8}\]  
Then, we perform indexing operations on the \(|\Delta Coords_{t}|_{sorted}\) to obtain the threshold \(T\). \[T=|\Delta Coords_{t}|_{sorted}[target]. \tag{9}\]  
Any point falling below this threshold will result in a mask with false values at the corresponding locations, thus ensuring the precise identification and handling of sub-threshold data points. To identify an appropriate threshold \(T\), we conducted an investigation using a mini dataset from nuScenes. We analyzed the changes in the number of points for foreground and background categories at a pruning ratio of 0.3, 0.5, and 0.7. The results revealed that when the pruning ratio \(T\) is around 0.3, there is a significant reduction in the number of background points, while the reduction in foreground points is minimal and can be considered negligible. Therefore, it is recommended that the pruning ratio \(T\) be set around 0.3. This mask clearly differentiates background and foreground points, facilitating accurate segmentation and analysis. Applying a mask allows the downsampling operation to be performed only on the selected points, significantly reducing the required computations, as shown in Figure 7. According to Equations (4) and (6), this progress can be expressed as follows. \[P_{dilate\_prune}=Mask(P_{in})-offset. \tag{10}\]  
Then, the output coordinates can be represented as:  
\[P_{out\_prune}=P_{dilate\_prune}\quad\text{if}\quad P_{dilate\_prune}\%\text{s} =0. \tag{11}\]  
As shown in Figure 8, the pruning method is applied on the first downsample layer of the encoder because the information of coordinate differences is more precise than after the downsampling operation, which generates new coordinate information that is harder to be used again to prune points.
## 3 Procedure, Structural Models, and Strong Motions
### Structural Models with W14 Columns  
The three-, nine-, and twenty-story buildings with PMRF considered in the SAC steel project [36] located in the Los Angeles area are used in this study; they are assumed to satisfy the code requirements existing at the time of the project (Uniform Building Code) [37]. These models are used for the following reasons: (a) they represent a standard design practice in the United States; (b) they were designed for experienced structural engineers in the United States for a very important project; and (c) they are widely used in numerical investigations. They will be referred to, in general, as the SAC models and will be particularly denoted hereafter as Models M1, M2, and M3, respectively. The first three lateral vibration periods of Model M1 are 1.05 s, 0.29 s, and 0.15 s, while those of Model M2 are 2.36 s, 0.79 s, 0.54 s. The corresponding values for Model M3 are 4.15 s, 1.34 s, 0.75 s. Their design base shear coefficients are 0.08, 0.04, and 0.03, for Models M1, M2, and M3, respectively, according to ASD-UBC 94. The damping is considered to be 3% of the critical (\(\zeta=3\%\)). The plan and the elevations of Models M1, M2, and M3 are given in Figures 1-3, respectively. In such Figures (plan), the I-shaped symbol indicates the orientation of the columns; the term \"4 bays @ 30'' means \"four bays spaced every 30 feet\". The axial loads and bending moments are studied for some exterior (EXT) and interior (INT) columns of the PMRF located at the ground-floor level, as indicated in the figures. The sections, including girders and columns, of Models M1 and M2 can be seen in Table 1, while those of
# High-Frame-Rate 3-D Vector Flow Imaging in the Frequency Domain
## 2 Materials and Methods
### 3-D Displacement Estimation  
In order to estimate the velocity components for a specific point of interest \(\rm P(x_{p},y_{p},z_{p})\) (i.e., the black dot in Figure 2), the volume of RF data is divided into parallelepipeds, hereinafter referred to as \"kernels\", containing \(\rm M_{B}\times N_{B}\times P_{B}\) pixels surrounding P. For each kernel, to estimate the average displacement between consecutive TX events, the 2-D VFI algorithm--proposed in [12] and recalled in Appendix A--is independently applied to the two perpendicular planes (yellow and purple in Figure 2) that cross the kernel's vertical (red in Figure 2) axis. Thus, the average (x, z) and (y, z) displacement components are obtained. Note that the displacement component along the z-axis is estimated twice, allowing a more robust assessment by averaging the two estimates. It is worth highlighting that kernels can be arbitrarily located (i.e., they can be either partially overlapped or isolated), only influencing the spatial resolution of vector velocity estimates. In this study, kernels were located on a regular grid and overlapped 80% and 90% in the axial and both lateral directions, respectively, thus giving the same spatial resolution of 0.3 mm along the three directions.  
Figure 1: Reference system for simulations and acquisitions. The volume of interest investigated by the 2-D plane wave is highlighted in light pink. The cylindrical pipe (light green) direction is defined by the beam-to-flow Doppler angle, \(\rm\vartheta_{D}\), and the azimuth rotation angle, \(\rm\vartheta_{A}\).  
Given that the developed algorithm is highly parallelizable, in order to compute the 3-D displacement for the whole volume and to keep the computational burden as low as possible, the processing is split into three steps. First, the 2-D VFI algorithm is applied to the planes perpendicular to the y direction to compute both the (x, z) displacement components (Figure 3A); then, the (y, z) components are similarly obtained by processing the data from planes perpendicular to the x direction (Figure 3B); finally, the velocity components corresponding to the same kernels are grouped (Figure 3C). Here, this method was implemented using the basic Matlab (The MathWorks, Natick, MA, USA) functions and by exploiting only the central processing unit (CPU) of the host computer.
## 5 Results
### Predicted Results of the Linear Machine  
It takes approximately 16 hours of training time for the proposed PIGAN. As for the evaluation process, the total magnetic field approximation time of the 30-step PMLSM model using the proposed PIGAN is 1 s; each step costs 33.3 ms on average. In comparison, the conventional 30-step FE simulation takes 45 s, and each step takes 1.5 s on average. Convergence is achieved in around 150 k to 200 k iterations depending on the network configuration, as shown in Figures 4-6.  
It can be seen in Figure 4 that the results using Pix2 Pix have a higher loss in MAE. The curves of the PSNR and SSIM also show the same conclusion.  
Although the image synthesis metrics of the final optimized models of both PIGAN and Pix2 Pix are very close, they differ significantly in the electromagnetic metrics. As shown in Table 5, for the x-axis thrust, with continuous current excitation, the FEM is calculated to be 190 N, the PIGAN is calculated to be 192 N, and the Pix2 Pix is calculated to be 150 N, with an error of 21.05%. The same is true for the peak current excitation, with an error of 3.16% for the PIGAN but of 15.79% for the Pix2 Pix. The specific PIGAN and FE calculations resulting in the air gap magnetic density distribution and thrust force on the \(x\)-axis are shown in Figures 7-10. This shows that the loss function based on physical information can benefit the magnetic field approximation.  
Figure 4: The MAE loss curve of the proposed PIGAN and Pix2 Pix.  
Figure 5: The PSNR curve of the PIGAN and Pix2 Pix.  
Figure 6: The SSIM curve of the PIGAN and Pix2 Pix.  
Figure 8: The prediction results and FEM results of thrust force when the PMLSM works with the continuous current (2.23 A).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multirow{2}{*}{**FEM**} & \multicolumn{3}{c}{**Prediction**} & \multicolumn{2}{c}{**Absolute Error (\%)**} \\ \cline{3-6}  & & **Pix2 Pix** & **PIGAN** & **Pix2 Pix** & **PIGAN** \\ \hline Continuous force @ 2.23 A & 190 N & 150 N & 192 N & 21.05\% & 1.05\% \\ Peak force @ 15.5 A & 950 N & 800 N & 980 N & 15.79\% & 3.16\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparative study of the magnetic field approximation for the simulation of the test PMLSM using FEM, Pix2 Pix, and our model.  
Figure 7: The prediction and FEM results of the (**a**) x-axis and (**b**) y-axis air gap flux density when the PMLSM works with the continuous current (2.23 A).
## 5 Conclusions  
The present study investigates the use of aerial thermal imagery for the evaluation of the energy performance of buildings, and it is based on a case study in Bologna (Italy). In particular, the main steps required to measure the surface temperature of roofs were examined, evaluating quantitatively the influence of atmosphere and surface emissivity on the retrieved values.  
Even if the physical model behind the radiometric calibration of thermal imagery is well established, its practical implementation requires several ancillary data, whose quality has a strong influence on the quality of the final products. The approach discussed here requires a model of the atmosphere at the time of the thermal flight, a digital surface model and a description of the surface materials in order to derive an emissivity map. Here, this map was derived by a supervised object-oriented classification of a very high-resolution multispectral satellite image. The presented results demonstrate the sensitivity of the model to each one of the mentioned elements. In particular, the most critical aspect is the identification  
Figure 9: Energy classification of buildings in the spatial decision support system (SDSS) tool developed within the EnergyCity Project.  
of the appropriate values of the surface emissivity; in fact, a decrease by 1% only of these values produces an average increase of the retrieved temperatures by about 0.3 \({}^{\circ}\)C. Furthermore, the accurate mapping of the roofing materials and the proper registration of this map with the thermal image require considerable investments.  
The sensitivity of the model to all of the mentioned factors and the fact that not all of the required ancillary information is always available prove how important the execution of ground surveys is to obtain reliable results. For the experimentations presented here, a full validation of the results with a statistically-significant number of ground control points (where the surface temperature is directly measured at the time of the flight) is still not possible. However, the comparison with five ground control points suggests that a precision of 1 \({}^{\circ}\)C is still difficult to achieve in complex environments.  
Applying the data produced by the calibrated thermal imagery, a model to estimate the energy performance of building quarters can be developed and integrated into a spatial decision support system. For this purpose, additional information is needed (e.g., geometric data or the applied energy source); these data are unfortunately not all available in the majority of the real cases. For a quick decision support system without an extremely high need of additional data, a compromise is inevitable with respect to accuracy. In any case, models at the district level are generally more appropriate than at the individual building level.  
In addition to that, the airborne thermal imagery can be an efficient tool for detecting hot spots and thermal bridges or for comparing roofs.  
It is also to be noted that such tools do not substitute energy experts, but can facilitate their work and make their decisions faster.
# Going Deeper into OSNR Estimation with CNN
###### Abstract
The study of [25] probes the trained kernels in convolutional layers, which explains why CNN can process raw data directly. CNN introduced in [26] develops a tradition model of pattern recognition with the idea of the training feature extractor itself. Based on the idea of CNN, deep convolutional neural networks (AlexNet) [27], networks using blocks (VGG) [28], network in network (NiN) [29], and so forth were proposed in succession and outperformed their predecessors on the ImageNet classification challenge. The trend of CNN has been increasing in size, including the depth and width of the network, to improve the performance at the cost of a surge in computation complexity. Overfitting simultaneously becomes another side effect. The sparsely connected architecture inside networks is the fundamental way to solve both problems mentioned above. However, the computation of infrastructures nowadays suffers from low efficiency in numerical calculation on the non-uniform sparse matrix. Therefore, the main idea of GoogLeNet in [30] focused on finding out how an optimal local sparse structure in a convolutional vision network can be approximated and covered by readily available dense components, and the inception module was also raised in this paper. Apart from that, the structure of parallel branches itself is thought-provoking as well. In this paper, we dig deeper into OSNR estimation with CNN to realize end-to-end monitoring. A structure comprised of the Inception block, which is a critical component in GoogLeNet, is designed and explicated. Section 2 presents a review of the definition of OSNR and high-level consideration in CNN and Inception block. The ideas and principles behind the essential components in neural networks helped us to design the proposedscheme in Section 2.4. In Section 3, an experiment is set up to train and test the proposed scheme. The results are analyzed together with the architecture itself. Section 4 discusses the extent of training with the help of a learning curve. A comparison between the proposed scheme with state-of-the-art schemes in performance and complexity is presented as well. These come to a conclusion in Section 5.