esa-sceva/satcom-chunk-collection huggingface dataset:
# Mechanical Performance Analysis and Experimental Study of Four-Star-Type Crank-Linkage Mechanism
## 1 Introduction
Different dynamics may be encountered for different research targets, and theoretical models can predict the system's dynamic behavior through simulations and experimental studies. Control of mechanical vibration in reciprocating air compressors is a complex process. Factors such as the unbalance of inertia force and torque in the crank-connecting rod, overturning torques due to gas forces, vibrations caused by motor operation, and vibrations due to cyclic intake and exhaust from the compressor all contribute to the overall vibration noise level of the air compressor [9]. When conducting multibody dynamics analysis on the four-star crank-connecting rod mechanism, the existence of a gap is usually ignored and ideally processed. However, in actual engineering, gaps formed due to part manufacturing, assembly of the motion pair, and wear and tear during operation are inevitable [10]. The collision forces caused by the motion pair gap not only consume the mechanical system's energy but also induce vibration, noise, and dynamic output in the system. In high-speed, heavy-load machinery, gaps can lead to violent contacts and collisions between elements of the motion pair, with reaction forces rising to several times their normal values, inducing greater deformation, wear, and increased vibration noise and seriously affecting the efficiency and lifespan of the machine [11]. The key to analyzing the dynamics of a mechanism with a gap motion pair is to establish a relatively accurate gap motion pair model and integrate it into the system dynamics model to obtain a dynamic model that includes the gap. What are the dynamic characteristics of a four-star-type crank-linkage mechanism? What is the difference in the force of flexible and rigid crank linkage on the main bearing? How can we model the dynamics of a revolving pair with clearance? What are the characteristics of the dynamic characteristics of the crank linkage with clearance? To address these questions, the mechanical characteristics of the four-star-type crank linkage are analyzed and experimentally studied in this paper. Firstly, we simulate the motion of piston and linkage at all levels of air compressor, derive the inertia force and moment of inertia of the four-star-type crank linkage, calibrate the static strength of some parts, and perform modal analysis on the split structure of the crank linkage. Then, we establish the crank-linkage rigid-flexible dynamics model to simulate the force on the main bearing under the action of ideal gas force for the rigid-flexible coupling crank linkage and the kinematic pair of crank linkage with friction. Moreover, the analysis of the characteristics of the four-star-type crank linkage with clearance is carried out. Finally, we design and build a four-star-type crank-linkage test bench independently to analyze the effects of wear clearance and rotational speed on the acceleration response of the slider and the bench.
# A 3D Vision-Based Weld Seam Extraction Method for Arbitrarily Placed Spatial Planar Weldments
## 2 Improved RANSAC for Plane Segmentation  
### Three-Dimensional Reconstruction  
To obtain the 3D model that represents the global geometric features of a weldment, we grab multiple point clouds of the weldment, which is randomly fixed on a turntable and rotates with it. Three-dimensional reconstruction is carried out by point cloud registration on all the point clouds. The setup is depicted in Figure 1.  
For any point fixed on the turntable, the coordinates with respect to the turntable system and the world base system (also the camera system) are \({}_{t}x\) and \(x\), which satisfy  
\[x=\mathbf{P}(\mathbf{n})_{t}x+c, \tag{1}\]  
where \(\mathbf{n}\) is the coordinate of the rotation axis, \(\mathbf{P}(\mathbf{n})\) is the angle transform matrix between the two bases and \(c\) is the coordinate of the turntable center point with respect to the world base.  
After the turntable rotates about its \(z\) axis (with respect to the turntable system) with angle \(\theta\), the coordinate becomes  
\[\begin{array}{ll}{}_{t}x_{\theta}&=R(z,\theta)_{t}x\\ \Rightarrow x_{\theta}&=\mathbf{P}(\mathbf{n})_{t}x_{\theta}+c\\ &=\mathbf{P}(\mathbf{n})R(z,\theta)_{t}x+c\\ &=\mathbf{P}(\mathbf{n})R(z,\theta)\mathbf{P}^{-1}(\mathbf{n})(x-c)+c\\ &=R(\mathbf{n},\theta)x+(\mathbf{I}-\mathbf{R}(\mathbf{n},\theta))c,\end{array} \tag{2}\]  
Figure 1: Capturing point clouds of the weldment on a turntable.  
where  
\[\mathbf{R}(z,\theta)=\begin{pmatrix}\cos\theta&-\sin\theta&0\\ \sin\theta&\cos\theta&0\\ 0&0&1\end{pmatrix}, \tag{3}\]  
and  
\[\mathbf{R}(n,\theta)=\mathbf{P}(\mathbf{n})\mathbf{R}(z,\theta)\mathbf{P}^{-1}(\mathbf{n}) \tag{4}\]  
are the rotation matrices with respect to rotation axis \(z\) and \(\mathbf{n}\), respectively.  
So, the registered point cloud of multiple frames grabbed on the turntable can be expressed as  
\[C_{\theta}=\bigcup_{j}\bigcup_{k}\bigl{[}\mathbf{R}\bigl{(}\mathbf{n},\theta-\theta_{ j}\bigr{)}x_{k}+\bigl{(}\mathbf{I}-\mathbf{R}\bigl{(}\mathbf{n},\theta-\theta_{j}\bigr{)} \bigr{)}c\bigr{]}, \tag{5}\]  
where \(\theta_{j}\) is the angular position of the weldment in frame \(j\) and \(\theta\) is that of the registered point cloud; \(x_{k}\) is the position of the \(k\)th point in the point cloud.
## 3 Results
### Characterization of The Compounds  
Two phytotoxic substances were identified by bio-guided fractionation from the leaf extract of \(G\). _xanthochymus_ by spectral analysis. The formula of the substance 1 was assigned as C\({}_{8}\)H\({}_{8}\)O\({}_{4}\) based on HRESIMS at m/z 167.0349 [M-H]- (calcd for C\({}_{8}\)H\({}_{7}\)O\({}_{4}\), 167.0344, \(\Delta\) = +0.5 mmu); \({}^{1}\)H NMR (400 MHz, D\({}_{2}\)O) \(\delta_{\rm H}\) 7.52 (d, \(J\) = 7.50 Hz, 1 H, H-2), 7.45 (dd, \(J\) = 8.5, 2.0 Hz, 1 H, H-6), 6.93 (d, \(J\) = 8.5 Hz, 1 H, H-5), 3.90 (s, 3 H, H-8); \({}^{13}\)C NMR (100 MHz, D\({}_{2}\)O) \(\delta_{\rm C}\) 173.5 (C-7), 147.9 (C-4), 146.7 (C-3), 128.1 (C-6), 123.2 (C-1), 114.8 (C-5), 113.1 (C-2), 55.8 (C-8). Comparing this spectral data with previously published data substance was recognized as vanillic acid with the systematic name 4-hydroxy-3-methoxy benzoic acid shown in Figure 3 [18].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Aqueous Methanol Extracts of \(G\). _xanthochymus_ (mg Dry Weight Equivalent Extract/mL) \\ \hline
**Test plant Species** & **Shoot** & **Root** \\ \hline Lettuce & 7.13 \(\pm\) 0.94 & 4.73 \(\pm\) 0.61 \\ Rapeseed & 17.20 \(\pm\) 0.68 & 14.23 \(\pm\) 0.85 \\ Italian ryegrass & 15.84 \(\pm\) 0.78 & 8.53 \(\pm\) 0.99 \\ Timothy & 7.13 \(\pm\) 0.72 & 4.81 \(\pm\) 0.97 \\ \hline \hline \end{tabular}
\end{table}
Table 1: \(I_{50}\) values (concentrations causing 50% growth inhibition) of _Garcinia xanthochymus_ leaf extracts (mg dry weight equivalent extract/mL) on lettuce, rapeseed, Italian ryegrass, and timothy \(\pm\) standard deviation.  
Figure 1: Effect of the \(G\). _xanthochymus_ extracts on the shoot and root growth of (**A**) lettuce, (**B**) rapeseed, (**C**) Italian ryegrass, and (**D**) timothy at different concentrations. Mean \(\pm\) SE was calculated from two independent experiments (Replication = 3 times, number of seedlings/treatments = 10, n = 60). Asterisks denote significant variations between indicated plants (**_p_ \(<\) 0.01 and ***_p_ \(<\) 0.001).  
The molecular formula of the substance 2 was assigned as C\({}_{10}\) H\({}_{12}\)O\({}_{3}\) based on HRES-IMS at m/z 181.0830 [M + H]\({}^{+}\) (calcd for C\({}_{10}\) H\({}_{13}\)O\({}_{3}\), 181.0865, \(\Delta=-3.5\) mmu); \({}^{1}\)H NMR (400 MHz, CDCl\({}_{3}\)) \(\delta_{\rm H}\) 7.07 (d, J = 8.7 Hz, 2 H, H5, 9), 6.76 (d, J = 8.7 Hz, 2 H, H6, 8), 3.67 (s, 3 H, H10), 2.88 (t, J = 7.8 Hz, 2 H, H3), 2.60 (t, J = 7.8 Hz, 2 H, H2). The substance was identified as methyl phloreate (3-(4-hydroxyphenyl) propionic acid methyl ester) (Figure 3) by investigating original data with previously documented literature [19].
## 2 Analysis of Traffic Load Effect of a Super-Long-Span Suspension Bridge
The selected effect influence lines and the calculated effects are shown in Table 1.  
According to the loading results, the extreme load effects of the mid-span displacement of the main beam and the axial force of the main cable at the saddle are both less than that of the corresponding specification value, while the axial force of the suspenders is greater than their specification value, indicating that the specification load provides a sufficient safety reserve for the main beam and the main cable. However, for the suspender, when the actual traffic load level is high, the limit set by specification may be exceeded. The further analyzing the source of this difference, we can see from the shape of the influence lines of each effect in Table 1 that the performance of the main beam and main cable is related to the overall structural system, and it is less affected by the action of the local vehicles, and the influence lines are spread over the whole span and are relatively gentle. Because the suspender is directly connected to the bridge deck under traffic load, its influence line of axial force has a prominent peak and steep distribution, with non-negligible coordinate values only in a limited range. The result of the difference in the shape of the influence lines is that, for the main beam and the main cable, the effect is mainly affected by the uniformly distributed load, which is loaded along the whole bridge span, while the suspender is mainly affected by the heavy vehicles acting nearby. Therefore, \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Mid-span Displacement of** & **Axial Force of Main Cable** & **Axial Force of Suspender at** & **Axial Force of Suspender at** \\  & **Main Beam (m)** & **at Saddle (N)** & **L/4 * (N)** & **L/2 * (N)** \\ Influence line & & & & \\ \(E_{\text{CODE}}\) & \(-3.23\) & \(5.05\times 10^{7}\) & \(3.94\times 10^{5}\) & \(4.31\times 10^{5}\) \\ \(E_{\text{EV}}/E_{\text{CODE}}\) & \(-2.11\) & \(3.10\times 10^{7}\) & \(4.94\times 10^{5}\) & \(5.19\times 10^{5}\) \\ \(E_{\text{EV}}/E_{\text{CODE}}\) & \(65.36\%\) & \(61.31\%\) & \(125.42\%\) & \(120.49\%\) \\ \hline \hline \end{tabular}  
* * L is the length of the middle span.  
\end{table} 
Table 1: Traffic load effect of main components. the suspender is a component with a higher degree of sensitivity to a heavy concentrated force. At the same time, for the super-long-span suspension bridge, the number of suspenders is large, and the position and length of the suspender and other factors lead to differences in the load response to vehicles. Under the action of the same traffic flow, some suspenders can be safe, while the other suspenders are faced with an insufficient safety reserve. Therefore, it is necessary to classify this large number of suspenders before studying its traffic load effect and applicable load model.
# Assessment of Lightweight Concrete Thermal Properties at Elevated Temperatures
## 2 Materials and Methods
### Methodology
#### 2.3.3 DSC Technique  
The DSC technique is a specific type of calorimetry, which uses both a sample material and a reference material. DSC measures the amount of heat required to increase the temperature of a sample and a reference. The reference sample should have a well-defined heat capacity over the range of temperatures to be scanned. The samples and the reference material follow very similar temperature curves throughout the experiment. This method has been widely employed in recent decades for studying phase transitions, such as melting or exothermic decompositions [34; 35; 36].  
The Netzsch STA 449 F3 equipment is a simultaneous thermal analysis (STA) device based on standards ISO 11358 [37], DIN 51006 [38], and DIN 51007 [39]. It combines thermogravimetry (TG) and differential scanning calorimetry (DSC) in a single test to measure mass change and heat flow as functions of temperature. This equipment was used to determine \(c_{p}\) and perform the experimental campaign. Netzsch STA 449 F3 is able to test in a range of temperature between 30 and 1500 \({}^{\circ}\)C in an oxidative or inert atmosphere. The temperature and balance resolutions are 0.001 K and 0.1 \(\upmu\)g, respectively, over the entire weighing range. The DSC enthalpy accuracy is \(\pm\)2% for most materials.
## 2 Multiple Damage Location Assurance Criterion (MDLAC)  
Structural damage detection (SDD) techniques are generally classified into two main categories. They are the dynamic and static identification techniques, which, respectively, need dynamic and static test data. Furthermore, compared with static identification approaches, dynamic identification methods have proven to be superior [36]. Among the dynamic data, the modal analysis information of a structure such as the natural frequencies and mode shapes have been widely used for SDD [36; 37]. The level of correlation between measured and estimated natural frequencies and/or mode shapes can be used to locate and evaluate structural damage. When the natural frequencies are employed to identify the damage, two parameter vectors are defined. One parameter vector consists of the ratios of the first \(n_{f}\) natural frequency changes \(\Delta F\) due to structural damage, i.e.:  
\[\Delta F=\frac{F_{h}-F_{d}}{F_{h}}, \tag{1}\]  
where \(F_{h}\) and \(F_{d}\) denote the natural frequency vectors of the healthy and damaged structure, respectively. Another parameter vector can be defined similarly as:  
\[\Delta F(ESP)=\frac{F_{h}-F(ESP)}{F_{h}}, \tag{2}\]  
where \(F(ESP)\) is a natural frequency vector that can be predicted from an analytic model and elemental stiffness parameters (ESPs) \(ESP=[E_{1},\ldots,E_{i},\ldots,E_{n}]^{T}\), which represents a damage variable vector containing the elasticity modulus of structural elements \((E_{i},i=1,\ldots,n)\) of all \(n\) structural elements.  
There are numerous approaches to evaluate the level of correlation given a pair of parameter vectors. The multiple damage location assurance criterion (MDLAC), which is presented in the following form, is an effective technique to analyze a correlation index [38]:  
\[MDLAC(ESP)_{F}=\frac{\left|\Delta F^{T}\Delta F(ESP)\right|^{2}}{(\Delta F^{T} \Delta F)(\Delta F^{T}(ESP)\Delta F(ESP))}, \tag{3}\]  
The MDLAC analyzes two frequency change vectors, one from the inspected structure and the other from the analytical model. The MDLAC ranges from 0 to 1. It will be at its maximum when the analytical frequency vector equals the frequency vector of the damaged structure, i.e.:  
\[F(ESP)=F_{d}, \tag{4}\]  
In addition to using the natural frequencies to identify structural damage, the mode shape vectors have been used in several studies. [39; 40]. In this case, the correlation coefficient of Equation (3) can be expressed as:  
\[MDLAC(ESP)_{\varphi}=\frac{\left|\Delta\varphi^{T}\Delta\varphi(ESP)\right|^{ 2}}{(\Delta\varphi^{T}\Delta\varphi)(\Delta\varphi^{T}(ESP)\Delta\varphi(ESP ))}, \tag{5}\]  
where \(\Delta\varphi\) is the vector of the measured mode shape changes that have a dimension equal to the product of the number of measured modes and sensor locations; and \(\Delta\varphi\) is the vector of the analytical mode shape changes with the same dimension as \(\Delta\varphi\). In this paper, an objective function is defined based on a combination of \(MDLAC(ESP)_{\varphi}\) and \(MDLAC(ESP)_{F}\).
# Demosaicing by Differentiable Deep Restoration
## 4 Experiments
### Reconstruction from Noisy Data  
Real images are often contaminated by noises. In this experiment, we assume additive Gaussian noise [53] and experiment different methods under different noise levels. We use an image with additive Gaussian noise to generate the raw image \(M\). Our goal is to reconstruct the original image \(I\) from the noise corrupted data.  
We experimented 5 different levels of Gaussian noise. To verify the capability of our method on noisy data, without any modification on network architecture, we directly train our demosaicing network with jointly learned CFA from scratch on the noisy image. In the training stage, the input image is contaminated with a random level of Gaussian noise. Then the trained network is tested on noisy data contaminated by these 5 levels of Gaussian noise separately. Specifically, the standard deviations \(\sigma\) of the Gaussian noise is set to \(\{4,8,12,16,20\}\).  
Figure 6 compares the PSNR of our method with other techniques on the test set of MIT datset [9] corrupted by different levels of Gaussian noise. We can see that our method presents good generalization capability for different levels of noise with a single model. It outperforms other joint denoising and demosaicing methods, even the denoising problem is not explicitly considered in our network design.  
We also show the reconstructed color image from noisy data in Figure 7. The first and last columns are the corrupted image and the clean reference image, respectively. The other four columns are results from different methods. We can see that our method can successfully reduce the noise in the corrupted image. It presents clear color reconstruction without oversmoothing the high frequency content.  
Figure 6: PSNR comparison on the test set of MIT dataset [9] corrupted by Gaussian noise with standard deviations \(\sigma\) of \(\{4,8,12,16,20\}\). Our method is compared with joint demosaicing and denoising methods [PERSON] et al. [2], [PERSON] et al. [52], [PERSON] et al. [9] and [PERSON] et al. [3].
## 3 Proposed Methodology
### Overall Routing Process
Descriptions of the computation of the above parameters are described in Section 3.3;
* Once the link delay matrix (\(D[N,N,2]\)) and normalized network contention (\(\mathcal{C}\)) are found, the routing algorithm will choose the mode as highest stable least distance or highest stable least delay by comparing \(\mathcal{C}\) with \(\mathcal{C}_{th}\). However, unlike the initial cycle, routes will be computed, and flow table entries will be updated using the adaptive flow rule computation and update algorithm only upon reception of packet_in messages. As was described in the explanation of that algorithm, each packet_in message may not result in computing and updating the flow table at the controller. If the flow table entry corresponding to the packet_in message is already updated, that entry will be sent to the corresponding switch to update its flow table using a FlowMod packet.
## 5 Experimental Results and Discussion
### Discussion  
The main observations from our study indicate that model performance varies significantly between the original and augmented datasets. On the original dataset, all models demonstrated strong performance for the Fatal and Personal Injury classes but struggled to classify the \"Property Damage\" classes. For instance, the XGB-TFIDF model achieved an F1 score of 0.9768 for \"Fatal\" and 0.9954 for \"Personal Injury\" but 0.0 for \"Property Damage\". This pattern was consistent across other models, highlighting the challenge of classifying minority classes in imbalanced datasets. In contrast, the augmented dataset showed improved performance for minority class classification, particularly for the \"Property Damage\" class. The XGB-TFIDF model, for example, achieved an F1 score of 0.5122 for \"Property Damage,\" demonstrating the effectiveness of data augmentation.  
Based on the performance metrics, the best three models selected for the ensemble hard voting were XGB-TFIDF, BERT-LSTM, and RoBERTa-LSTM. These models were chosen due to their superior performance across both datasets. For instance, BERT-LSTM achieved an F1 score of 0.98 for \"Fatal\" and 0.56 for \"Personal Injury\" on the original dataset while improving to 0.98 for \"Fatal\" and 0.96 for \"Personal Injury\" on the augmented dataset. Similarly, the RoBERTa-LSTM model consistently performed well, with an F1 score of 0.99 for \"Fatal\" and 1.00 for \"Personal Injury\" on the original dataset and maintaining high scores on the augmented dataset. These selections were justified by their robust performance and ability to handle the increased complexity of the data, thus making them ideal candidates for ensemble learning.  
The ensemble model's performance on both the original and augmented datasets underscores its effectiveness. On the original dataset, the ensemble model achieved high precision, recall, and F1 scores for the \"Fatal\" and \"Personal Injury\" classes, with F1 scores of 0.99 and 1.00, respectively, but failed to classify the \"Property Damage\" class, resulting in an F1 score of 0.0. In contrast, on the augmented dataset, the ensemble model showed significant improvement, with the \"Property Damage\" class achieving an F1 score of 0.62 and macro and weighted average F1 scores increasing to 0.87 and 0.98, respectively. These results demonstrate the ensemble model's robustness and enhanced capability to handle imbalanced datasets through data augmentation. Despite these improvements, the performance for the \"Property Damage\" class remains lower than desired, indicating a need for further research to refine augmentation techniques and model architectures to address this challenge better.
## 2 Materials and Methods
### Spectral Acquisition  
Spectral acquisition requires a scan configuration (see Figure 3). Texas Instruments provides two different scan configurations within the \"Factory\" settings: \"Column\" and \"Hadamard\".  
Column scanning selects wavelengths one at a time. With Hadamard scanning, several wavelengths are multiplexed at a time, and the wavelengths are decoded individually. Hadamard scanning collects light, and offers a higher SNR, but it depends heavily on the spectrum being measured, and on the measurement system [15]. Column analysis is more effective for analyzing forage samples, because it is more accurate than other methods of analysis, due to its reproducibility.  
Before scan collection, one to five sections can be configured, depending on the type of method (Column or Hadamard), the spectral range (the starting wavelength and ending wavelength), the digital resolution (the wavelength points captured within the defined spectral range) the exposure time (between 0.635 and 60.960 milliseconds), and the number of scans per sample (in this work, 10 scans at 10 different points in the sample).  
In this work, all samples were scanned in reflectance mode, using the measurement system shown in Figure 2.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Parameter (\%)** & **Mean** & **Min.** & **Max.** & **SD** \\ \hline NDF & 40.70 & 29.24 & 61.01 & 5.50 \\ MC & 10.64 & 8.47 & 13.71 & 0.91 \\ CP & 14.48 & 7.19 & 17.01 & 1.70 \\ \hline \hline \end{tabular}  
* NDF: neutral detergent fiber; MC: mineral content; CP: crude protein.  
\end{table}
Table 2: Statistics for the nutritive value of the alfalfa samples (N = 57).  
Figure 3: NIRscan Nano GUI scan screen (reflectance signal).  
In order to increase the sampling window and improve the spectroscopic information for each sample, after the alfalfa samples were homogenized, each was divided into three subsamples and scanned using miniaturized NIRs. Each spectrum is an average of 10 spectra, in a wavelength range between 901 and 1700 nm, with a non-linear path wavelength between 2.9 and 3.9 nm. A total of 30 scans were collected for each alfalfa sample. The precision of the collected spectrum for each sample or signal reproducibility was evaluated for the raw and ground alfalfa. The statistic of the root-mean-square error (_RMSE_) was calculated. Using (1), it is possible to calculate the _RMSE_ for each sample spectrum. Lower _RMSE_ values are related to reproducible and repeatable spectra.  
\[RMSE=10^{6}\times\sqrt{\frac{\sum D^{2}}{n}}\ ;D=y_{a}-y_{b} \tag{1}\]  
\(y_{a}\) = log 1/R to \(\lambda\) for the average spectrum resulting from averaging a number of scans, and R is reflectance.  
\(y_{b}\) = log 1/R to \(\lambda\) for the average spectrum resulting from averaging \(b\) number of scans  
\(n\) = number of spectral data