esa-sceva/satcom-chunk-collection huggingface dataset:
# Smart Manufacturing Scheduling Approaches--Systematic Review and Future Directions
## 4 Main Findings
### Manufacturing Scheduling Requirements
#### 4.1.9 Transportation  
Product parts need to be moved inside the factory from one machine to another, or to the storage zone. This process can involve transportations through conveyors, robotic arms, automated guided vehicles, and many other solutions [27]. This means that first the product will not be immediately available in the next machine and a certain time is required to transport it, and second, the number of transporters is limited. So, they must be synchronized with the scheduling process along the chain or the parts need to wait for an available one, which may require to allocate each job to a transport vehicle and sequence the transport tasks to be executed by each vehicle [50]. However, transportation times arenot often considered in the scheduling problems found in the literature, which can severely affect the scheduling performance.
## 3 Spectrum Management Issues
### M-Milmo
#### 3.5.3 Receivers Design  
As for the M-MIMO system, Huawei and NTT Docomo have conducted massive field trials to evaluate the feasibility of M-MIMO for MU-MIMO schemes for future wireless communication [231]. They have modeled three types of MIMO precoding schemes, such as linear precoding: Eigen Zero-Forcing (EZF), nonlinear precoding: Tomlinson Harashima Precoding (THP), and hybrid precoding. By implementing the methodology, the results have proved that the hybrid scheme brings positive outcomes due to its litheness on selecting EZP and THP to take benefits of both schemes in a practical scenario. The authors in [232] have presented an uplink spectrum efficiency approach for the M-MIMO system. The TDD realization is used based on the Zero-Forcing (ZF) and Maximum Ratio Combining (MRC) schemes. The results have shown that spectral efficiency is significantly improved, and the design condition is dependent on the number of antennas at the BS and pilot reuse factor.
# Attacking Robot Vision Models Efficiently Based on Improved Fast Gradient Sign Method
## 1 Introduction
Inspired by the RMSProp algorithm, we efficiently propose an improved RMSProp-based fast gradient sign method, RMS-FGSM, to attack robot vision models. RMS-FGSM generates adversarial examples in an iterative manner. At each iteration, an adaptive learning rate is used to update adversarial examples. Our learning rate is computed based on the EWMA algorithm, which smoothes the change of gradients by considering historical data. Since our method can mitigate the problem of the gradient's steep growth, adversarial examples can be updated along a stable optimization direction, and the optimal value is highly likely to be achieved; that is, adversarial examples with imperceptible perturbations and a high attack success rate. The experiments in this study are carried out with several common classification models in robot vision and the environment of Python 3.9, torch 1.13.1, and TorchVision 0.14.1. Two commonly used datasets, MNIST [18] and CIFAR-100 [19], were used. To validate the method, we attacked LeNet [20], Alexnet [1], and Resnet-101 [21]. Experimental results persuasively verify that RMS-FGSM has better performance than other advanced attack methods. Above all, the perturbation of the adversarial examples generated using RMS-FGSM is smaller than other methods under the same attack success rate. The rest of this study is arranged as follows. Section 2 introduces related work, including the FGSM, BIM, MI-FGSM, and NI-FGSM algorithms. The attack algorithm RMS-FGSM is described in Section 3. In Section 4, we present and analyze the experimental results. And the last section concludes the study.
# Structural Modal Time Domain Identification Method Based on the Bayesian Uncertain Quantification
## 3 Bayesian Modal Identification Based on Modal State Space Prediction Models
### Modal Parameters Identification Algorithm Flow  
Based on the method proposed in this paper, the main flow of identifying the modal parameters is as follows: first, the optimization objective function \(f\left(\left\{f_{i},\zeta_{i}\right\}\right)\) is established based on the measured response data; second, the MPVs of the natural frequencies and damping ratios are calculated by minimizing the objective function using the optimization algorithm, and, in turn, analytically solving for the MPVs of the modal contribution factors, mode shapes, and variance of the prediction error; finally, the Hessian matrix is solved using the finite difference method, and the inverse of the Hessian matrix is used to obtain the posterior covariance matrix. This process is illustrated in Figure 1.  
For the optimization problem in step 2 of Figure 1, the Matlab function \"fmincon\" was used to find the optimal solution of the spectral parameters that minimizes the objective function. This function can effectively deal with complex multivariate optimization problems and has a high solving efficiency.
## 3 Data Analysis and Model Training
### Sensitivity Analysis  
It is reasonable to posit that monitoring indicators should exhibit a stronger correlation with the risk of rock burst. Consequently, prior to the establishment of the model data set, data that are unrelated to the risk of impact should be excluded. The Pearson correlation coefficient method and the Spearman correlation coefficient method were employed to analyze the correlation between the monitoring data and the risk of rock burst. The Pearson correlation coefficient is defined as follows [31]:  
\[\rho_{XY}=\frac{Cov(X,Y)}{\sqrt{D(X)}\sqrt{D(Y)}}=\frac{E(X-EX)E(Y-EY)}{\sqrt{ D(X)}\sqrt{D(Y)}} \tag{14}\]  
In the aforementioned Formula (14), \(E\) is the mathematical expectation; \(D\) is the variance; and \(Cov\) (\(X\),\(Y\)) is the covariance in the sum of random variables, which is used to measure the overall error between the two variables. \(\rho_{XY}\) is the value of the quotient of covariance and standard deviation between two variables, also known as the correlation coefficient between variables \(X\) and \(Y\). The closer \(\rho_{XY}\) is to 1, the greater the correlation between the two variables is, and when it is equal to 0, the two variables are not related.  
The Spearman correlation coefficient is defined as follows [32]:  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{1}{c}{**Time**} & \multicolumn{1}{c}{**Daily Total Energy**} & \multicolumn{1}{c}{**Daily Maximum Energy**} & \multicolumn{1}{c}{**Daily Frequency**} & \multicolumn{1}{c}{**Daily Average Energy**} \\ \hline
1 June 2021 & 33,100 & 28,000 & 9 & 3678 \\
2 June 2021 & 120,000 & 90,000 & 13 & 9231 \\
3 June 2021 & 30,000 & 15,000 & 10 & 3000 \\
4 June 2021 & 15,000 & 4400 & 8 & 1875 \\
5 June 2021 & 30,000 & 19,000 & 7 & 4286 \\
6 June 2021 & 288,000 & 240,000 & 10 & 28,800 \\
7 June 2021 & 69,000 & 35,000 & 16 & 4313 \\
8 June 2021 & 270,000 & 180,000 & 21 & 12,857 \\
9 June 2021 & 91,000 & 53,000 & 16 & 5688 \\
10 June 2021 & 14,000 & 8400 & 8 & 1750 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Microseismic monitoring data.  
\[\rho=1-\frac{6\sum D^{2}}{N(N^{2}-1)} \tag{15}\]  
\(\rho\) represents the Spearman correlation coefficient. For values of \(\rho\) between \(-1\) and \(1\), the closer the absolute value of \(\mid\rho\mid\) is to \(1\), the greater the correlation. When the absolute value of \(\mid\rho\mid\) is equal to \(0\), the correlation is \(0\). \(D\) represents the difference between two data sequences, while \(N\) denotes the number of data points.  
The correlation coefficient diagram calculated by Pearson correlation coefficient and Spearman correlation coefficient is shown as Figure 4. It can be seen that the daily total energy, daily frequency, and daily maximum energy are positively correlated with the impact risk, and the correlation between the daily average energy and the impact risk is close to \(0\). Therefore, the model in this paper selects daily total energy, daily frequency, and daily maximum energy to establish a data set.
## 2 Materials and Methods
For test 4, the heaters of the PMP #2 system were switched off to measure the total particle number concentration at the dilution tunnel (nonvolatiles plus volatiles). Test 6a was conducted with PMP #2 sampling from the end of the transfer tube. The test was repeated (test 6b) setting the heater of the 4 m tube to 150 \({}^{\circ}\)C, instead of 80 \({}^{\circ}\)C that was the default set point for all other tests. The concentrations of the CPCs were corrected for the dilution in the PMP systems and the average particle losses at 30 nm, 50 nm, and 100 nm, the so-called particle concentration reduction factor (PCRF). When the heaters were off, the PCRF was corrected 10% for the lower thermophoretic losses and 45% for the different densities at the primary diluter, resulting in a 35% lower PCRF [12]. The penetration after correction with the PCRF is approximately 100% \(\pm\) 5% for sizes \(>\)30 nm. It drops to 50% at 15 nm, 25% at 10 nm, and \(<\)5% at 5 nm [10]. The concentrations given by the 10 nm and 4 nm CPCs are underestimated approximately by a factor of 2 for sizes below 30 nm, because the losses in the sub-30 nm range were not considered [6; 10]. However, as the objective of the paper is to understand \begin{table}
\begin{tabular}{c c c c} \hline
**Test** & **Test Configuration** & **PMP \#1 at Position 1 \({}^{\ast}\)** & **PMP \#2 (Position)** \\ \hline
1,5,7 & Default & Heaters on & Heaters on (position 3) \\
2 & Open transfer tube & Heaters on & Heaters on (position 3) \\
3 & Cold dilution TP & Mixing diluter 6:1, Heaters on & Heaters on (position 3) \\
4 & Cold dilution CVS & Heaters on & Heaters off (position 3) \\
6 & End transfer tube & Heaters on & Heaters on (position 2) \({}^{\ast}\) \\ \hline \end{tabular} \({}^{\ast}\) The PMP #1 at position 1 had additionally a 0.5 m heated (120 \({}^{\circ}\)C) tube, except at “Cold dilution TP” where it was switched off and was measuring downstream of the mixing diluter. PMP #2 at position 2 was used with the 0.5 m heated (120 \({}^{\circ}\)C) tube. CVS = Constant volume sampling; PMP = Particle Measurement Programme; TP = Tailpipe.  
\end{table} 
Table 1: Chronological order and experimental details of the various test configurations. the formation of the nonvolatile particles which have an order of magnitude higher concentration, the loss correction (which is identical for both PMP systems) does not have any influence on the results and conclusions. The corrected concentrations of the CPCs of PMP #1 connected to the tailpipe and of PMP #2 when connected at the end of the tailpipe were multiplied with the moped exhaust flow rate (approximately 0.2 m\({}^{3}\)/min). PMP #2 when connected to position 3 was multiplied with the CVS flow rate (approximately 5.5 m\({}^{3}\)/min) to calculate particles per second (particles/s) (see calculation details elsewhere [14]).
## 4 Results
### Orientation and Gradient of In Situ Stress  
Based on imaging logging, the orientation of the horizontal principal stress in the Mahu Sag can be accurately and efficiently determined. During the drilling process, the original stress balance in the formation is disrupted. Consequently, in situ stress is redistributed around the shaft lining. This phenomenon, known as stress concentration, may expand the cross-sectional area of wellbores. According to this theory, the relationship between the orientation of fractures and the strike of the horizontal principal stress can be determined. In this research, the orientation of drilling-induced fractures and wellbore collapse was determined from FMI logging. As illustrated in Figure 14, the orientation of \(\sigma_{\text{hmax}}\) and \(\sigma_{\text{hmin}}\) is approximately \(28^{\circ}\) and \(297^{\circ}\), respectively. In areas where fractures exist, the stress magnitude at the same well depth decreases.  
Acoustic emission tests, beneficial indicators illustrating general fluctuations of in situ stress, demonstrate that the stress gradient of \(\sigma_{\text{hmax}}\) in the target layer ranges from 1.89 to  
Figure 14: (**A**) DIFs in the resistivity image log. (**B**) Rose diagram of the strike of drilling induced fractures. (**C**) Rose diagram of the strike of wellbore collapse.  
Figure 13: The distribution of Poisson’s ratio. (**A**) 3D Poisson’s ratio model. (**B**) Planar distribution of Poisson’s ratio of the P\({}_{1}\)f\({}^{1}\).  
1.97 MPa/hm, while the \(\sigma_{\rm hmin}\) gradient varies between 1.72 and 1.80 MPa/hm (Table 4). However, due to sample limitations, the range of stress gradients calculated by the experiments may not completely represent the overall trend in the study area. Utilizing logging data to calculate the in situ stress gradient is advisable to address deficiencies in the experiment. Based on logging evaluation and experimental analysis, it can be concluded that the stress gradient of the maximum principal stress in the study area is 1.95-2.32 MPa/hm, while that of the minimum horizontal principal stress is 1.62-1.95 MPa/hm (Figure 6).
## 6 Discussion  
Establishing a digital twin model requires high-precision structural and operational data of the wind turbine spindle bearing, which imposes stringent demands on modeling accuracy, data acquisition, and processing capabilities. To create accurate digital twin models in Unity 3D, detailed structural data of wind turbines are required, including mechanical structure, physical characteristics, and dynamic simulation data. This not only requires precise data but may also necessitate the transformation of raw data to fit Unity 3D's modeling framework, a process that is both complex and time-consuming. Transfer learning models are often difficult to interpret. In practical industrial applications, particularly in wind turbine fault diagnosis, model interpretability is crucial for improving user trust and facilitating fault repair. However, transfer learning models typically lack sufficient transparency and comprehensibility, limiting their generalization in practical applications.  
The application of this method in real industrial environments may encounter challenges and limitations, with the model's migration performance potentially influenced by data noise, label scarcity, and changes in the operating environment. While advanced technologies like digital twinning and the efficient channel attention mechanism are used to improve the accuracy and efficiency of fault diagnosis, real-time data acquisition and processing in actual wind farms remain limited by hardware resources, computing power, and communication bandwidth.  
Future studies will focus on optimizing this system and integrating a broader range of data to enhance the robustness of the fault diagnosis process. The fault diagnosis system could also benefit from incorporating digital twinning and transfer learning methods using additional input types, such as the frequency domain, time-frequency domain, and signal images. Introducing multi-source domain transfer learning methods for knowledge sharing and migration between multiple source domains could further improve cross-domain adaptation, particularly across different wind turbine types and operating environments. Consider simplifying the model or integrating edge computing technology to enable rapid fault diagnosis and early warning for field equipment in the wind farm. Additionally, the processing and management of real-time data, along with the real-time mapping of physical wind turbines and virtual models, will be the focus of future research. These improvements will further enhance the reliability and practicality of the digital twin fault diagnosis system, facilitating its widespread application in the wind turbine industry.
# Machine Learning-Based Calibration of Low-Cost Air Temperature Sensors Using Environmental Data
###### Abstract  
The measurement of air temperature is strongly influenced by environmental factors such as solar radiation, humidity, wind speed and rainfall. This is problematic in low-cost air temperature sensors, which lack a radiation shield or a forced aspiration system, exposing them to direct sunlight and condensation. In this study, we developed a machine learning-based calibration method for air temperature measurement by a low-cost sensor. An artificial neural network (ANN) was used to balance the effect of multiple environmental factors on the measurements. Data were collected over 305 days, at three different locations in Japan, and used to evaluate the performance of the approach. Data collected at the same location and at different locations were used for training and testing, and the former was also used for \(k\)-fold cross-validation, demonstrating an average improvement in mean absolute error (MAE) from 1.62 to 0.67 by applying our method. Some calibration failures were noted, due to abrupt changes in environmental conditions such as solar radiation or rainfall. The MAE was shown to decrease even when the data collected in different nearby locations were used for training and testing. However, the results also showed that negative effects arose when data obtained from widely-separated locations were used, because of the significant environmental differences between them.  
sensor calibration; low-cost sensor; machine learning; artificial neural network; agriculture 2017
## 3 DPD Coefficients Recognition Algorithm
### The JSG-DPD Indirect Learning Structure
There is a mode that can be set to avoid the problem of temporary degradation of communication quality: when the received signal quality degrades, only the pilot signal used for updating DPD coefficients is transmitted. Normal scientific data will be transmitted after the DPD coefficients have been updated. Hence, although it takes time to upload DPD coefficients, it is not required for each transition, and the number of uploaded coefficients is extremely small.
## 1 Introduction
The surface wave can be muted from the observed data, but it requires a manual cost; and (3) Kirchhoff migration is limited by the degradation in the imaging resolution that occurs when the wavelength is not much smaller than the size of the imaging area in near-surface detection. In the medical field, the widely applied ultrasound beamforming method, which detects human body structures using acoustic properties, gives us another possible way to acquire seismic signals and solve the abovementioned problems. The delay and sum (DAS) beamforming algorithm is the most popular beamforming technique for imaging human body structures because of its real-time imaging capability [19; 20]. In recent decades, various adaptive beamforming methods have been proposed to improve the resolution and stability of beamformers in medical ultrasound imaging. The most common approaches are based on the minimum variance (MV) beamformer devised by [PERSON] [21], and numerous MV beamforming algorithms have been developed to continue improving the imaging quality [22; 23; 24; 25]. [PERSON] [26] proposed an MV beamforming method combined with adaptive coherence weighting and achieved an excellent performance in an application to medical ultrasound imaging. [PERSON] et al. [27] introduced the multiple delay and sum with enveloping method to efficiently suppress sidelobes and artifacts. Most recently, [PERSON] et al. [28] proposed the multioperator MV adaptive beamformer to promote real-time imaging. Accordingly, because the transmission and receiver concepts in medical imaging and seismic reflection are similar, the medical beamforming method can be adapted for seismic imaging. In this study, we propose a minimum variance spatial smoothing (MVSS) beamforming method for near-surface reflection seismic exploration in a homogeneous assumption. Synthetic near-surface geological models are established to carry out a numerical simulation, and the image quality of the proposed MVSS beamforming method is compared with that of Kirchhoff migration and basic DAS beamforming. Moreover, the robustness to interferences and noise, robustness to other wave components and a delay time correction to enhance the focus effect are presented. Finally, the computational efficiency, a comparison between MVSS beamforming and RTM (one of the best imaging methods for seismic data), and the potential application of the CF matrix in time domain prestack migration methods are discussed.
# A Spam Filtering Method Based on Multi-Modal Fusion
## 4 Experimental Results and Discussion  
### Corpus  
In this paper, we choose three types of email datasets for our experiments: the dataset only contained text, the dataset only contained image and the mixed dataset that contains image and text data. The dataset only containing text comes from the Enron corpus [29], and we only choose 6000 text emails (4500 Spam, 1500 Ham) by removing duplicates and randomly selecting from 33,645 text emails. The dataset only containing images is composed of Personal Image Ham, Personal Image Spam and Spam Archive Image Spam [30] after culling the data with a picture size is equal to 0 and simply getting rid of the duplication images. Since there is no public mixed dataset that contains both image and text data, in order to get the mixed dataset, we choose to combine the Enron dataset with Personal Image Ham, Personal Image Spam and Spam Archive Image Spam, eliminating invalid data and randomly selecting them to form the mixed dataset 1 and 2. The dataset details used in the experiments are shown in Table 5 below.  
For the mixed dataset 1, the number of text dataset is equal to the number of image dataset, which contains 600 Spam (text Spam 600 and Image Spam 600 are formed into 600 spam) and 600 Ham (text Ham 600 and Image Ham 600 are formed into 600 Ham email). For the mixed dataset 2, the number of text dataset is not equal to the number of image dataset, which contains 600 Spam (through by dropout technology, text Spam 600 and Image Spam 300 are formed into 600 Spam) and 600 Ham (through by dropout technology, text Ham 600 and Image Ham 300 are formed into 600 Ham). We selected 5-fold cross-validation method to evaluate the MMA-MF model performance. From the principle of k-fold cross-validation method introduced in Section 3.2, we can derive the size of the training and testing datasets for the four datasets that shows in Table 6. Since each text email and image email are only mixed once to get one mixed email, this ensures that the training and test datasets do not overlap at all.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Type** & **Original Dataset** & \
\begin{tabular}{c} **Before Remove** \\ **Duplicates** \\ \end{tabular} &
\begin{tabular}{c} **After Remove** \\ **Duplicates** \\ \end{tabular} \\ \hline \multirow{2}{*}{Text} & Enron Ham & 17,108 & 1500 \\  & Enron Spam & 16,537 & 4500 \\ \hline \multirow{3}{*}{Image} & Personal Image Ham & 2021 & 1393 \\  & Personal Image Spam & 3298 & 130 \\  & Spam Archive Image Spam & 16,031 & 1263 \\ \hline \multirow{2}{*}{Mixed Dataset 1} & Ham:Enron\&Personal Image & 19,129 & (Text:Image) 600:600 \\  & Spam:Enron\&Personal Image\&Archive Image & 35,866 & (Text:Image) 600:600 \\ \hline \multirow{2}{*}{Mixed Dataset 2} & Ham:Enron\&Personal Image & 19,129 & (Text:Image) 600:300 \\  & Spam:Enron\&Personal Image\&Archive Image & 35,866 & (Text:Image) 600:300 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Datasets used in experiments.