esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Temperature Tide Identification
### Tide Histograms  
Concerning migrating modes, tidal characteristics are supposed to be similar along a single latitude. Due to the random error associated with the time of maximum (phase), histograms of tidal anomalies show a Gaussian-type shape allowing us to extract estimates of the mean value and an associated uncertainty of the phase. In the example shown in Figure 2 top, time of maximum for diurnal migrating tides is, as expected, around 18 h, with an uncertainty of \(\pm 2\) h. However, in many other cases, histograms exhibit multi-modal distributions (see Figure 2 middle and bottom) with main and secondary modes that are possibly associated with non-migrating tides located at some specific locations or simply due to uncertainties in deriving tidal characteristics when amplitudes are small. Each maximum of the distribution has been fitted with Gaussian envelopes, allowing us to derive for each latitude one or two tidal phases and amplitudes as well as uncertainties depending on the width of the individual Gaussian.  
The method used is called KDE (kernel density estimation). This method, also called the Parzen-Rosenblatt method [30], makes it possible to estimate the probability density of a variable. It is based on a sample population and estimates the population density at all sample points. This method is frequently used to generalize an estimation by histogram. The KDE is defined by two parameters, the kernel (K) and the window (\(h\)), according to the formula of the density probability given below:  
\[d_{h}(x)=\frac{1}{N.h}\sum_{i=1}^{N}K\frac{(x-x_{i})}{h}. \tag{2}\]We are looking for Gaussian (normally distributed) modal distributions, and a Gaussian kernel with the following form is retrieved:  
\[K=\frac{1}{\sqrt{2\pi}}\text{exp}^{(-\frac{1}{2}x^{2})}. \tag{3}\]  
To avoid the hassle of setting the fit, an alternative method was used. This is called the Gaussian mixture model [31]. It makes it possible to model several Gaussian modes and to extract the weight of each mode in the distribution, its mathematical expectation, and its variance. Both methods give similar results. However, because the number of iterations is the only parameter required to be fixed, the second method was better adapted for implementation in the analysis of a large data set. An example of the fit is represented in Figure 2. Histograms reveal that, several diurnal or semi-diurnal tidal components can be observed.
###### Abstract  
Given that the fault information of motor bearing is submerged due to strong noise under variable load conditions, a fault diagnosis method of motor bearing based on parameter optimization variational mode decomposition (VMD) and normalized least mean square (NLMS) is proposed. Firstly, VMD's modal number K and \(\alpha\) penalty factor are optimized by symbolic dynamic entropy (SDE). Then, the VMD algorithm with optimized parameters is used to extract the fault signals of bearing inner and outer rings under different load conditions. Then, the appropriate intrinsic mode decomposition (IMF) is selected, according to the weighted kurtosis index to reconstruct the fault feature signals. Finally, the NLMS algorithm reduces noise in the reconstructed signal and highlights the fault characteristics. The fault characteristics are analyzed by envelope demodulation. The RMSE and SNR of the simulated signal are calculated by filtering the improved method. It is found that the RMSE of the filtered signal is reduced 60%, and the signal-to-noise ratio is increased by about 119.87%. Compared to the sparrow search algorithm (SSA)-optimized VMD method, the proposed approach shows significant improvements in fault feature extraction. This study provides an effective solution for motor bearing fault diagnosis in noisy and variable load environments.  
variational mode decomposition (VMD); symbolic dynamics entropy (SDE); normalized least mean square (NLMS); bearing fault diagnosis; variable load conditions +
Footnote †: journal: Applied sciences  
Article
## 2 Materials and Methods
### Analysis of Odor-Active Compounds in CSSs  
2.4.1 GC-O Analysis  
GC-O was performed by means of an Agilent 7890 GC combined with an olfactory detection port (ODP3, Gerstel, Germany) and an FID (Agilent Technologies, USA). The GC effluent at the end of the capillary column was split into a 1:2 ratio by volume using a Y-typesplitter and two uncoated deactivated fused silica capillaries between the FID and ODP. To maintain the nose sensitivity, the sniffing port was coupled with humidified air. The temperatures of the GC injector port, the FID, the transfer line of ODP3 and the olfactory port were 250 \({}^{\circ}\)C, 280 \({}^{\circ}\)C, 250 \({}^{\circ}\)C and 220 \({}^{\circ}\)C, respectively. The extracts were analyzed on both a DB-Wax column and a Hp-5 MS column (Agilent, both are 30 m \(\times\) 0.25 mm \(\times\) 0.25 \(\upmu\)m). When the DB-Wax column was used, the oven temperature was held at 40 \({}^{\circ}\)C for 2 min, increased to 80 \({}^{\circ}\)C at a rate of 8 \({}^{\circ}\)C/min, increased to 100 \({}^{\circ}\)C at a rate of 4 \({}^{\circ}\)C/min, then rose to 230 \({}^{\circ}\)C at a rate of 6 \({}^{\circ}\)C/min, and finally held at 230 \({}^{\circ}\)C for 5 min. When the Hp-5 MS column was used, the oven temperature was held at 40 \({}^{\circ}\)C for 2 min, increased to 100 \({}^{\circ}\)C at a rate of 4 \({}^{\circ}\)C/min, ramped to 230 \({}^{\circ}\)C at a rate of 10 \({}^{\circ}\)C/min and finally held at 230 \({}^{\circ}\)C for 5 min. Ultra-high purity helium was used as the GC carrier gas at a constant flow rate of 1 mL/min. All concentrated fractions (1 \(\upmu\)L) or SPME isolates were injected in splitless mode. During GC-O analyses, three trained evaluators (two females and one male, who had been trained to sniff the aromas of reference compound solutions with different concentrations in the laboratory for at least 3 months) from Beijing Key Laboratory of Flavor Chemistry at Beijing Technology and Business University sniffed the odors of the effluent from the sniffing port. When evaluators detected the odor, they needed to record the retention time (RT) and the odor characteristics. Analyses were carried out three times by each evaluator.
# A Sensitivity Analysis of a Computer Model-Based Leak Detection System for Oil Pipelines
## 3 Results and Discussion
### Low \(R\) System vs. High \(R\) System  
The effect of uncertainties on leak detectability in systems with low and high \(R\) values was compared as they have distinctly different hydraulic responses. Computed leak detection times are summarized in Table 5 along with the corresponding viscosity error, bulk modulus error, and time skew for each case. The change in detection times relative to the corresponding baseline case are also tabulated, and negative numbers indicate earlier detection times compared to the baseline case. However, these were associated with excessive diagnostic flow caused by uncorrected errors rather than a leak. Note that setting \(VER\) to the same value in the two systems results in a significantly smaller \(FPDC\) bound in the low \(R\) system, as the portion of the bound that is amplified by \(VER\) is proportional to the flow rate. The magnitude of the correction that can be made to the viscosity error in the low \(R\) system is much less compared to the high \(R\) system. Therefore, \(VER\) was set to different values in the low and high \(R\) systems to obtain the same \(FPDC\) bound in order to make a fair comparison. \(VER\) was set to its maximum value of 100% in the system with \(R\) = 0.49, which produced an \(FPDC\) bound ranging from 0.153 to 0.162 depending on the flow rate after transient. This bound range was equivalent to \(VER\) of 2-14% in the system with \(R\) = 2.20.  
For the perfect data tests in Table 5, there was no clear indication of whether leak detectability was better in high \(R\) or low \(R\) systems. However, uncertainties had only a minor impact on leak detectability when the \"measured\" data contained no noise. Thus, it was not essential to determine the fine distinctions observed between the low \(R\) and high \(R\) systems. For noisy data tests, however, the change in detection time from the baseline case was smaller in the low \(R\) system when comparing to the corresponding case in the high \(R\) system for 11 of the 15 cases. For the remaining four cases, the two with time skew were undetected in both systems (i.e., \(DVB\) never crossed the threshold line); the two cases with positive viscosity error had larger detection time change in the low \(R\) system, but again, the changes were very  
Figure 9: Effect of time skew on leak detectability in a system with \(R\) = 2.20 with noisy data (sensors contain a 10-s time skew): (**a**) flow decrease; (**b**) flow increase; and (**c**) steady state.  
minor (within 1.5 min from the baseline). It can be concluded that in most cases, the impact of uncertainties is less in the low \(R\) systems compared to high \(R\) systems.
# PK-APF: Path-Keeping Algorithm for USVs Based on Artificial Potential Field
## 3 Experiments
### MATLAB Implementation
#### 3.1.1 Path Planning with Local Minimum Point
The statistics of path-keeping errors are shown in Figure 6. The maximum deviation of APF is 1.332 m when avoiding obstacles, and it cannot return to the original path in time. The maximum deviation of PK-APF is 0.8177 m, which is 38.61% less than that of APF. Besides, it only deviates from the planned path when avoiding obstacles. After that, it can return to the planned path in time without the influence of obstacles and achieve a higher precision of path-keeping compared to APF. As the PK-APF needs to return to the original path in time after leaving the influence range of the obstacle. The path length of PK-APF is larger than that of APF, and the average step size of PK-APF is the same as that of APF; so, the total time step of PK-APF is larger than that of APF.
# Effect of Fiber Wave Structure in Printed Circuit Boards on Signal Transmission Characteristics
## 3 Results and Discussion
### Methods to Improve Fiber Weave Effects
#### 3.4.3 Making Traces and Fiber Weave Bundles with an Angle  
Making signal traces and fiber weave bundles with an angle is a relatively simple and effective method to solve the differential skew problem [11; 12]. There are two practical methods to achieve this:  
(1) The PCB manufacturer rotates the image. This reduces the utilization rate of laminates and increases production costs. Usually, a 10 degrees rotation angle makes production costs increase 8-10%. For customers, this is a big increase;  
(2) The designer rotates the trace. This approach requires designers to rotate the signal trace during layout. This would increase the difficulty of layout and affects layout efficiency.  
Figure 14: \(\Delta\)Phase of Electronic-glass versus NE-glass.  
Figure 13: Impedance fluctuation of traces on NE-glass.  
Another question is what is the right angle. Here, we tested the effects of three kinds of rotation angles (5 degrees, 10 degrees, and 15 degrees). As can be seen from Figure 15, the \(\Delta\)Phase results of the three kinds of rotation angles were basically identical. This means it did not take too much of an angle between trace and fiber weave bundle to resolve the fiber weave problem. Theoretically, a trace merely has to cross two weave bundles along its length, so that the effect on the two adjacent traces is equalized (see Figure 16 and Equation (5)) [1]. According to the pitch size of the fiber weave bundles and trace lengths, the rotation angle could be calculated.
## 4 Performance Evaluation and Comparison
### Evaluation of Other Kernels Using Feature Extraction Approach  
In this subsection, feature extraction using convolution coefficients and cross-correlation coefficients as features is adopted. The following five kernels, linear, RBF polynomial and sigmoid kernel, and mixtures of polynomial and RBF kernels [31] are applied. They can be expressed by:  
\[\text{Linear kernel}:\ k_{1}(x_{i},x_{j})=\left\langle x_{i},x_{j}\right\rangle \tag{13}\]  
\[\text{RBF kernel}:\ k_{2}(x_{i},x_{j})=\exp(\left\|x_{i}-x_{j}\right\|_{2}/2\sigma) \tag{14}\]  
\[\text{Polynomial kernel}:\ k_{3}(x_{i},x_{j})=\left(\left\langle x_{i},x_{j} \right\rangle+c\right)^{p} \tag{15}\]  
\[\text{Sigmoid kernel}:\ k_{4}(x_{i},x_{j})=\tanh(\left\langle x_{i},x_{j} \right\rangle+c) \tag{16}\]  
\[\text{Mixtures of polynomial and RBF kernels }k_{5}(x_{i},x_{j})=\rho k_{3}(x_{i},x_{j})+(1-\rho)k_{4}(x_{i},x_{j}) \tag{17}\]Three scenarios are considered: (i) Only convolution coefficients serve as features (1-199 coefficients); (ii) Only cross-correlation coefficients serve as features (1-199 coefficients); and, (iii) Both convolution and cross-correlation coefficients serve as features (1-398 coefficients).  
Table 3 summarizes the performance of kernels \(K_{1}\)-\(K_{5}\) in three scenarios. Only the best scenario is given. The results reveal that scenario (iii) achieves highest performance, because it takes the advantages from both the convolution and cross-correlation coefficients. Compared \(K_{\text{CS}}\) with \(K_{1}\)-\(K_{5}\), the ranking (from highest to lowest) is \(K_{\text{CS}}>K_{5}>K_{3}>K_{2}>K_{4}>K_{1}\). When compared to scenarios (i), (ii), and (iii), \(K_{\text{CS}}\) improves the Acc by 14.4-58.2%, 12.4-59.6%, and 8.9-53.7%, respectively.
## 3 Proposed Method
### First Stage: Extracting an ROI  
ROI extraction aims to reduce background areas, remove information irrelevant to the segmentation process, and mitigate the imbalance of pixel classes by identifying and extracting the region containing the MYO, LVC, and RV. Consequently, this stage minimizes computational costs by avoiding processing the original-sized slices.  
The input images are first preprocessed. Due to our hardware limitations, they are resized to \(160\times 160\). Then, the pixel intensities are normalized to a range between 0 and 1. After preprocessing, the images are passed to the U-Net with VGG16 as the backbone [9; 45], whose blocks are composed of 2D convolutional layers with ReLu activation and Batch normalization, techniques commonly used to prevent overfitting.  
To simplify the learning process and due to the centralization of MYO in the medial slices, as can be seen in Figure 2, the U-Net was trained to generate masks specifically for the myocardium rather than all three structures. As only one region will be the focus of the segmentation, the complexity of this task will be decreased. Therefore, in this stage, U-Net will generate MYO masks for a given volume only for medial slices. These masks are called reference segmentations because they are the starting point for extracting the bounding boxes that will delimitate the ROI. In tests, this approach resulted in more accurate segmentation results. So, in this context, the last U-Net layer consists of the \(1\times 1\)convolution with the sigmoid activation function to perform the pixel-level classification between 0 (background) and 1 (MYO).  
Before the bounding-box generation, the predictions are eroded using a \(3\times 3\) squared structuring element to eliminate tiny disconnected artifacts. Subsequently, the method identifies the input slice with the largest area in its reference segmentation. A bounding box is then generated from the center of this slice, and its dimensions are adjusted based on the original slice size in order to include the structures of interest.  
After conducting tests, it was verified that bounding boxes with dimensions between \(120\times 120\) and \(160\times 160\) were defined. These bounding boxes are replicated throughout the volume and used as a reference for extracting the ROI. Finally, these ROIs are fed into the second and third stages of the proposed method.  
Figure 1: The proposed multi-stage method: (1) ROI extraction stage, (2) MYO and LVC segmentation stage, and (3) RV segmentation stage.  
Figure 2: Visibility of the structures of interest in the (**A**) basal, (**B**) medial, and (**C**) apical slices.
## 2 Study Area and Data  
### Location and Meteorology  
Tangshan City is situated in the northeast of Hebei Province, with geographical coordinates ranging from 117 deg31' to 119 deg19' east longitude and 38 deg55' to 40 deg28' north latitude. The total land area of Tangshan City is 13,472 km2, comprising 5131 km2 (38.09%)of mountainous terrain and 8341 km\({}^{2}\) (61.91%) of plains. Additionally, it has a sea area spanning 4472 km\({}^{2}\), with a coastline stretching 229.72 km in length. Tangshan experiences a typical warm temperate sub-humid continental monsoon climate characterized by cold and dry winters with northerly winds, and hot and rainy summers with southerly winds. The annual average temperature is 10.6 \({}^{\circ}\)C, while the average precipitation from 1956 to 2020 amounts to 608.7 mm.
# BIM-Based Model Checking: A Scientometric Analysis and Critical Review
## 5 Challenges and Future Directions
### Future Directions
#### 5.2.3 Interoperability of Data  
The interoperability of model data during retrieval and conversion processes is crucial for data exchange. This forms the foundation for the interaction between BIM models and other formats of documents (such as 2D drawings, GIS) or rule-based data. Moreover, the current AEC domain is transitioning from the text and 2D drawings to the multidimensional data, where the interaction and integration of multi-source information affect both BMC research and the development of the AEC field. However, the discussions on this topic are currently inadequate, so further research is necessary to ensure the completeness, accuracy, and efficiency of data circulation.  
The unification of the basic format for creating BIM models is a crucial prerequisite. Currently, there are numerous modeling software available for different demands, such as Autodesk Revit, Bentley, CATIA, and SketchUp. Some common 3D file formats, like \".fbx\" and \".obj\", can partially enable conversion between different models, but some local information may be lost. Data exchange that is based on proprietary file formats typically only works within different software produced by the same manufacturer. This means these formats usually cannot be directly converted with other BIM models. The significance of IFC is gradually becoming more apparent, as it has become an important foundation for software exchange and information sharing in construction projects. Additionally, the application of universal formats, such as Green Building XML (GBXML) and City Geography Markup Language (CityGML), as well as mainstream software, like Solibri Model Checker V9 and FORNAX, should be promoted to reduce heterogeneous data.
# On Sharing Spatial Data with Uncertainty Integration Amongst Multiple Robots Having Different Maps
## 5 Uncertainty Integrated Confidence Decay Mechanism with Extended Kalman Filter  
The integration of confidence decay meachansim in EKF is given in Algorithm 2. The algorithm is straightforward and estimates the Kalman gain (\(K_{t}\)), robot's pose (\(\mu_{t}\)) and the covariance (\(\Sigma_{t}\)) at time \(t\) until step 12. Later, Eigen values (\(\lambda_{1}\cdots\lambda_{n}\)) are extracted from the covariance matrix (\(\Sigma_{t}\)) by applying Singular Value Decomposition. The degree of the confidence curve is then determined using the amount of uncertainty represented by the Eigen values in steps 14 and 15 of Algorithm 2 as explained in the previous section. Essentially, the degree of the curve is chosen to fasten the confidence decay inversely proportional to the positional uncertainty.
# Automatic Identification of Peanut-Leaf Diseases Based on Stack Ensemble
## 4 Discussion  
Machine-learning models have been developed constantly and have become very effective in the classification of images. They provide a feasible method for the identification of most plant-leaf diseases. However, the effectiveness of machine learning on the identification of peanut-leaf diseases have not been previously reported. Accurate diagnosis of peanut-leaf disease classes, and timely treatment of the disease are practical guidance for peanut production.  
In this study, traditional machine-learning methods and deep-learning models were used for the identification of peanut-leaf diseases. After that, the traditional machine-learning methods and deep-learning models were combined by stacking ensemble. The training strategies of data augmentation and transfer learning were also used to evaluate the fitting performance of the models on the dataset of peanut-leaf diseases. The results showed that the deep-learning models had more robustness than the traditional machine-learning methods when identifying peanut-leaf diseases. The models with deeper network layers such as Densenet121 and Resnet50 performed better than shallow and wide networks when used as the base-model. The performance of logistic regression classification was worse than support vector machine and random forest classification, but logistic regression as the meta-model could best improve the accuracy and \(F1\) score of the base-model.  
In the literature, different traditional machine-learning methods of classification, such as clustering [10], stacking [12], and low-dimensional to high-dimensional mapping [11], have been shown to be applicable to the classification of a wide range of plant leaves. However, to obtain high accuracy and robustness, it is necessary to artificially extract features, and the quality of the extracted features directly affects the performance of the model.  
Deep-learning models have excellent performance in classifying images due to the rotation invariance, translation invariance, and sparse parameters of the convolutional layer [27]. [PERSON] verified that deep-learning models can be applied to disease identification in a variety of plant leaves [25]. In order to improve the accuracy of recognition, various deep-learning training strategies have also been successfully applied to plant image classification, such as data augmentation [23], transfer learning [21], and semisupervised learning [24]. The five selected deep-learning models in our study still had a good classification performance in the dataset collected in the field environment. The results showed that the deep-learning models were still able to accurately identify peanut-leaf diseases in complex picture backgrounds. The deep-learning model with deeper network layers fit better to the peanut-leaf-disease dataset used in this study. Moreover, when the deep-learning model was used as a meta-model, the better the classification performance it had, the better the performance it achieved after the stacking ensemble.