esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Force Modeling for Machining Stability Analysis
To determine the optimal feed rate for stable machining, the generated cutting force data undergoes Fast Fourier Transform (FFT) analysis. The FFT conversion allows for the identification of dominant frequency components in the cutting force signals. According to the principles of machining chatter, chatter vibrations occur at multiples of the spindle speed frequency. By analyzing the magnitude of these frequency components, the feed rate corresponding to the lowest vibration magnitude is selected as the optimal feed rate for each machining condition. In an ideal machining process without chatter, the primary frequency component in the FFT spectrum should concentrate on the tooth passing frequency:  
\[f_{c}=Nf_{s} \tag{9}\]  
where \(N\) is the number of cutter teeth and \(f_{s}\) is the spindle speed frequency. This frequency represents the periodic force fluctuations caused by the tool engaging with the workpiece. The spindle speed frequency, \(f_{s}\) represents the number of spindle rotations per second and is calculated as:  
\[f_{s}=\left(\Omega_{\text{rpm}}\times\frac{2\pi}{60}\right)_{\frac{\text{ud}}{ \text{ud}}}\times\frac{1}{2\pi} \tag{10}\]  
where \(\Omega\) is the spindle speed in revolutions per minute (RPM). This fundamental frequency serves as a reference for identifying chatter frequencies and their harmonics in the FFT spectrum. However, when chatter occurs, additional frequency components appear at integer multiples of the spindle speed:  
\[f_{c}=nf_{s},\ \ n=1,\ 2,\ 3,\ \ldots \tag{11}\]  
These additional peaks indicate unstable cutting conditions and are indicative of chatter-induced vibrations. To identify the optimal feed rate, the FFT spectrum is analyzed by evaluating the combined magnitude of frequency components at the first, second, and third harmonics of the spindle speed. Specifically, the total magnitude within a \(\pm 20\%\) range around each harmonic frequency \(nf_{s}\) (where \(n=1,\ 2,\ 3\)) is computed as:  
\[M_{total}=\sum_{n=1}^{3}\sum_{f=(nf_{s})\times 0.8}^{(nf_{s})\times 1.2}M(f) \tag{12}\]  
where \(M(f)\) represents the magnitude of the frequency component at frequency \(f\). The \(\pm 20\%\) bandwidth was selected to accommodate two key factors: (1) potential frequency shifts between simulated and measured dominant vibration modes due to modeling simplifications, and (2) spectral leakage effects commonly observed in FFT analysis. This margin ensures that harmonics with slightly shifted peaks are still captured, preserving robustness in chatter evaluation. For each set of machining parameters, the FFT spectrum is examined, and the feed rate that minimizes \(M_{total}\) is identified as the optimal feed rate, as it corresponds to the most stable cutting condition with the least chatter influence. The process iterates through all machining conditions, storing the optimal feed rate associated with each axial and radial depth of cut.
## 3 Results and Discussion
### Structural Characterization of JSP
#### 3.2.2 Monosaccharide Composition  
Monosaccharide composition of JSP was determined by GC/MS analysis after acidic hydrolysis. As shown in Table 2, JSP was mainly composed of galactose (62.35%), glucose (20.0%) and xylose (15.41%), but also small amounts of glucuronic acid (2.24%).  
Regarding the literature, galactose and xylose were the principal constitutive residues of polysaccharides extracted from red algae of the Corallinales order. Consequently, JSP could be a xylogalactan-rich fraction as already reported by [PERSON] and [PERSON] [3,18] for sulfated polysaccharides extracted from other corallinans, such as _J. rubens_ and _C. officinalis_. JSP presented a ratio of Gal\(p\)/Xyl\(p\) equal to 4.05 (Table 2). These results were in agreement with previous studies done on xylogalactans isolated from _C. officinalis_ (2.9-4.4), _J. rubens_ (2.6-3.5) and _B. orbigniana_ (2.9-4.4) [3]. The Gal\(p\)/Xyl\(p\) ratio found in this paper was considerably higher than those described for xylogalactans derived from _L. heteroclatum_ (1.5-3) [2] but lower than those recorded for sulfated galactans isolated from _Spyridia hyproides_ and _H. durvillei_, which contained high Gal\(p\)/Xyl\(p\) ratios (\(>\)10) [16,43].  
Finally, the presence of polluting floridean starch (as indicated by Glc residues) is common, considering the used extraction and purification procedures, and was largely described for other corallinans in the literature [3,18].
## 5 Conclusions  
This study revealed a substantial difference between phenology extracted using satellite-derived SIF and EVI data across areas limited by different climatic factors (temperature, radiance, water). We inferred that the differences between SIF-based phenology and EVI-based phenology have a close relationship with their different responses to climate limitations. The higher climatic limitation index could result in larger differences in phenology derived from SIF and EVI for each dominant climatic limitation area. The phenology extracted using SIF was more correlated with climatic limitations than that using EVI, especially in water-limited areas, making it the main cause of the difference between phenology extracted using SIF and EVI. These findings improved our understanding of land surface phenology from greenness and photosynthesis perspectives and provided insight into the mechanistic differences between SIF and EVI in characterizing land surface phenology.  
Conceptualization, Q.H.; methodology, C.W. and S.L.; validation, Y.W. and Q.X.; resources, J.H. and Y.C.; writing--original draft preparation, C.W.; writing--review and editing, Q.H., Y.W., S.L., Q.X., J.H. and Y.C. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the National Natural Science Foundation of China (42101391, 41901380), the Young Elite Scientists Sponsorship Program by CAST (2020 QNIRC001), the \"Soil and Water Conservation Monitoring Research in Hubei Province\" project (2017052601) funded by Hubei Anyuan Safety and Environmental Protection Technology Co., Ltd. (Wuhan, China), the Fundamental Research Funds for the Central Universities (CCNU21 XJ028, CCNU2 QQN032), and the fellowship of the China Postdoctoral Science Foundation (2021M701361). [PERSON] acknowledges support from the University of Technology Sydney Chancellor's Postdoctoral Research Fellowship.  
Not applicable.  
The data used to support the findings of this study are included within the article.  
The authors declare no conflict of interest.
## 2 Materials and Methods
### Datasets and Preprocessing
#### 2.2.3 Data Processing  
All indexes were uniformly processed as raster images using the WGS1984 coordinate system, with a temporal resolution of annual and a spatial resolution of 1 km. Initially, to enhance pixel reliability, missing data and low-quality pixels were identified based on quality marker bands. The missing positions were filled using adjacent pixels interpolation and the Savitzky-Golay filter. Subsequently, the temporal resolution was standardized to an annual scale. NDVI and LAI indicators were synthesized using the maximum value method. GPP and PM2.5 were computed as their annual averages, and annual precipitation was determined as a 12-month sum using the cumulative method. Importantly, all datasets were normalized to the range from 0 to 1. Different methods were applied based on the attributes and physical meaning of the indicators (as detailed in Table 2). The relationship between the assessment indicators and ecological quality can be categorized as positive, negative, appropriate, and corresponding.  
Positive relationship: The larger the indicator is, the better ecological quality will be.  
\[X_{i}^{\prime}=\frac{X_{i}-X_{imin}}{X_{imax}-X_{imin}},(i=1,\;2,\ldots n) \tag{1}\]  
Negative relationship: The larger the indicator is, the worse ecological quality will be.  
\[X_{i}^{\prime}=\frac{X_{imin}-X_{i}}{X_{imax}-X_{imin}},(i=1,\;2,\ldots n) \tag{2}\]  
Appropriate relationship: There is an optimal threshold for the indicator, such as climate factors, and the more it exceeds or falls below the threshold, the worse the ecological quality.  
\[X_{i}^{\prime}=\left\{\begin{array}{ll}\frac{x_{i}}{x_{0}}&x_{i}<x_{0}\\ 1&x_{i}=x_{0},(i=1,\;2,\ldots n)\\ 2-\frac{x_{i}}{x_{0}}&x_{i}>x_{0}\end{array}\right. \tag{3}\]  
Corresponding relationship: Only for LC data, a specific category corresponding to a defined scalar value according to previous studies [59; 60].  
\[X_{i}^{\prime}=f(x_{i}),\;(i=Class_{1},Class_{2},\ldots Class_{n}) \tag{4}\]  
where \(X_{i}^{\prime}\) denotes the normalized value of the \(X_{i}\) indicator, \(X_{imin},\;X_{imax}\) indicate the minimum and maximum values of \(X_{i}\) within the study area, respectively, \(x_{i}\) means the initial value of indicator data, \(x_{0}\) denotes the appropriate threshold value artificially set, and \(f\) means the correspondence function between data categories and normalized values.
# Image-Based Feature Representation for Insider Threat Classification
## 2 Related Work
Maliciousness was predicted from the way a player plays the game. The paper explains several variations of the BAIT algorithm, amongst which the best approach gave a precision of only 0.07 and a recall of 0.7. Many works, like [PERSON] [19], exist, in which they propose approaches for insider threat detection based on the behavioral analysis of people in an organization. Several surveys [20; 21] have been reported in the area of insider attacks. [PERSON] et al. [20] mentions about different kinds of insider attacks, including, traitors, and masqueraders. The paper focused on the challenges of insider threat and categorized most popular techniques into Host-based User Profiling, Network-Based Sensors, and Integrated Approaches. One of the main challenges with the research in this direction is the lack of real-world data to study common solutions and general models. [PERSON] et al. [22] proposed a sliding window based approach to the insider threat problem using CERT CMU dataset and the Random Forest algorithm. They used 40% of the users amongst the 2000 users and applied the statistical feature normalization. Random Forest was used for classification, and the results were marked based on precision and F1-score. The main challenge in insider threat analysis is the need for algorithms and approaches that can efficiently identify malicious activities with reduced false negatives and false positives in optimal time [8; 23; 24]. There are existing works that have effectively applied image classification approaches and deep learning models for malware detection in the domain of cybersecurity. Transfer learning has been applied to malware classification [25] with an accuracy of 98.65%. [PERSON] and [PERSON] [26] proposed malware detection by representing the machine code as grayscale images and extracting intensity-based and texture-based features. This approach achieved an accuracy of 95% on a dataset containing 12,000 benign samples and 25,000 malware. [PERSON] et al. [27] developed a malware detection method that uses RNN to perform feature extraction. The extracted features are represented as images, and a CNN is used for classification to malicious or non-malicious, and achieved 96% accuracy. Recently, [PERSON] et al. [28] used the image-based transfer learning for malware analysis. There are papers, like Reference [29; 30; 31], that proved the effectiveness of image-based deep learning approaches for malware analysis. However, image classification has not been used for insider threat analysis. In the next section, we discuss our proposed method.
## 3 Results and Discussion
### Total Porosity  
Porosity results for the ten mixtures under study are presented in Figure 8. In the case of mixtures with RCA, it can be seen that for percentages of substitution of 20, 30, and 60, porosity does not vary significantly (2%, 4%, and 11%, respectively) with respect to the reference mixture (NAT) values. Porosity of the RCA-100 mixture is 125% higher than the NAT mixture. Regarding the mixtures with RFA, it can be observed that in RFA-20 the porosity increases 41%, while for RFA-30, RFA-60, and RFA-100, the porosity increases 65%, 91%, and 98%, respectively, with respect to NAT. The other reference mixture (RA-100) has a 138% higher porosity.  
This result indicates that RAC is usually more porous and has greater ability to absorb water, and this is attributed to the high absorption and the porous nature of the old mortar adhered to the RCA and RFA.  
As shown in Table 2, in comparison with natural aggregates, RA are more porous and old cement paste is attached on their surface. Thus, the microstructure of ITZ in RAC is different from that in conventional concrete [29], and the total porosity and the average porosity diameter of the concrete increases as the RA content increases [10; 30].  
In some investigations [10; 24], similar values and trends of porosity were found as a function of RCA amount in concrete. In other works [27; 31; 32], it was found that in concrete with RFA, the amount of water absorbed (implicit porosity) increases.
# Principles of Different X-ray Phase-Contrast Imaging: A Review
## 2 Methods
### Edge Illumination (EI)  
Edge illumination X-ray phase-contrast imaging was proposed by [PERSON] et al. in 2001 [132]. In this method, the incoming X-ray beam is collimated into one direction by a slit usually sized from a few to several tens of microns, and is aligned with an opaque edge placed right in front of the detector. As shown in Figure 15a, part of the X-ray beam is blocked by the edge after being refracted by the object, and the rest hits the detector. The position of the X-ray beam on the detector shifts owing to refraction from the sample, causing an intensity change in the detector. If the beam penetrates the sample, an image showing a mixture of absorption and refraction contrasts is obtained [133; 134; 135; 136; 137].  
Figure 14: Sketch of propagation-based imaging setup.  
This method can also be applied to conventional X-ray sources if appropriate masks with multiple apertures are employed, as shown in Figure 15b.  
This method was first demonstrated with a synchrotron source and was later performed with incoherent conventional laboratory sources [138]. The angular sensitivity of this method has been studied as a function of the experimental parameters and source intensity for synchrotron [134] and laboratory [135] sources. In 2012, [PERSON] et al. demonstrated that this method visualized the cartilage layer in slices of an excised rat tibia and correctly detected surgically induced lesions [139]. In 2014, [PERSON] et al. studied the spatial resolution of this method based on various experimental parameters [136]. The CT mode of EI was discussed and performed with a synchrotron source in the same year [140]. Nylon, PEEK (polyether ether ketone), PBT (polybutylene terephthalate), sapphire, titanium wires and coronal slices from a domestic wasp were imaged. In 2016, [PERSON] et al. obtained an angular-resolved small-angle X-ray scattering distribution with this method based on an X-ray tube. Absorption, differential phase, and scatter images were obtained with improved contrast, allowing for pulmonary emphysema diagnosis in a murine model [141]. In 2019, [PERSON] et al. performed this method in two- and three-dimensional modes (planar image and CT). Both the PMMA (polymethylmethacrylate) phantom and human breast tissue were imaged. The results showed that a more compact system provides a higher contrast-to-noise ratio (CNS), making it possible for this method to be applied to in situ surgeries [142]. The advances of this method were discussed by [PERSON] et al. in 2017 [137].  
This method provides higher contrast and better angular-resolution for low-Z materials such as human tissue. Because the sample mask blocks part of the X-ray beam, there is comparatively less dosage with this method. It has huge potential in medical science.
## 2 Methods
### Path Optimization Integrating Bezier Curve and Radial Basis Function Neural Network
#### 2.5.2 Radial Basis Function Neural Network  
When the coordinates of control points are known, it is easy to represent the shape of the Bezier curve. However, it is very difficult to obtain the coordinates of control points by the curve shape, which can be solved by the strong learning ability of neural network. The radial basis function is applied to the design of the neural network and forms the radial basis function neural network, which has good nonlinear approximation ability, fast training speed, and no local optimal problem. It is a three-layer forward network, with only one hidden layer. The first layer is input layer, which is composed of signal source nodes. The second layer is the hidden layer, the transformation function of the hidden element is radial basis function, and the number of hidden elements is determined by the need of solving the problem. The third layer is the output layer, and the output layer node is a simple linear function. The output of the hidden layer unit is weighted linearly to get the network output. The transformation between input layer and hidden layer is nonlinear, and the transformation from hidden layer to output layer is linear. As shown in Figure 15, the established radial basis function neural network has 101 input nodes, taking the slope of 101 points in the initial path; \(n\) hidden layer units; 4 output layer nodes, namely the horizontal ordinates of the four control points in middle of the fifth order Bezier curve. Network input \(x=[x_{1},x_{2},x_{3},\ldots,x_{101}]\), network output is \(y=[y_{1},y_{2},y_{3},y_{4}]\).
## 3 Materials and Methods
### Semi-Supervised Learning  
The 500 new mammographic images were divided into 224 \(\times\) 224 pixel patches, and the 38,211 unlabeled patches generated were used to infer the presence or absence of calcification with ResNet50, which had been trained on the labeled data. The number of patches was then determined by extracting the patches when the classification probabilities exceeded 0.80, 0.85, 0.90, 0.95, and 1.00. The patches that satisfied each classification probability underwent the following data augmentation processes. Initially, the brightness of the pixels in the original patch was reduced to 0.5 and 0.75 times their original value. Additionally, the original patch was rotated by 90\({}^{\circ}\), 180\({}^{\circ}\), and 270\({}^{\circ}\), respectively. Then, after flipping the original patch, the flipped patch was also rotated by 90\({}^{\circ}\), 180\({}^{\circ}\), and 270\({}^{\circ}\). Through these image augmentation processes, the number of patches was increased tenfold. The extended patches with high classification probabilities were then used for transfer learning to update existing classification models through semi-supervised learning. This was intended to incorporate the feature from the confidently classified unlabeled patches, aiming to improve the model's performance and generalization capabilities without requiring additional labeled data. The number of transfer-learned patches was calculated for each classifier. The patches without calcification were extracted to match the number of patches with calcification that satisfied the classification probability so that the number of patches with calcification and without calcification used for transfer learning would be equal. The patches without breast tissues were excluded. Figure 2 shows a schematic of the transfer learning process used in this study.  
\begin{table}
\begin{tabular}{c c} \hline \hline  & **Parameters** \\ \hline CNN a & ResNet50 \\ Mini batch size & 128 \\ Max epochs & 10 \\ optimizer & SGDM b \\ Initial learning rate & 0.001 \\ \hline \hline \end{tabular} a Convolutional neural network, b stochastic gradient descent with momentum.  
\end{table}
Table 7: CNN and parameters used to create the classifiers.  
Figure 1: A schematic of data augmentation.
# Efficient End-to-End Sentence-Level Lipreading with Temporal Convolutional Networks
## 7 Conclusions  
This paper proposed an efficient end-to-end sentence-level lipreading architecture, using an efficient feature extractor that combines 3D convolution and ResNet50 [37], and replacing the traditional recurrent neural network with a Temporal Convolutional Network (TCN) [36]. Finally, an end-to-end sentence-level lipreading architecture was trained using the CTC objective function [17]. The proposed architecture overcomes the difficulties of slow convergence, disappearing gradient, and poor performance. Experiments on the GRID dataset show that, compared with the state-of-the-art method, the performance accuracy increase by 2.4%, and the convergence speed increase by 50%.  
We divide our future work into three directions: first, the CTC objective function [17] used by the proposed architecture is based on independent conditional probability. Therefore, our research will focus on proposing a solution to this defect. Secondly, we can fully integrate voice features and visual features to seek further breakthroughs in performance. Finally, due to the shortcomings of long text samples in lipreading, designing a set of efficient and long-term dependent decoders is our future research direction.  
Conceptualization, T.Z., L.H. and G.F.; methodology, T.Z. and L.H.; software, L.H. and X.L.; validation, T.Z., L.H. and G.F.; formal analysis, L.H.; investigation, T.Z. and L.H.; resources, T.Z.; data curation, L.H.; writing--original draft preparation, T.Z. and L.H.; writing--review and editing, L.H. and X.L.; visualization, T.Z. and L.H.; supervision, T.Z.; project administration, L.H.; funding acquisition, T.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Zhejiang Lab's International Talent Fund for Young Professionals.  
**Institutional Review Board Statement:** Not applicable.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Seen(WER)** & **Unseen(WER)** & **Seen(CER)** & **Unseen(CER)** \\ \hline A & 1.5\% & 8.9\% & 1.6\% & 5.6\% \\ \hline B & 1.4\% & 7.9\% & 1.4\% & 5.1\% \\ \hline C & 1.3\% & 8.1\% & 1.3\% & 5.2\% \\ \hline D & 1.1\% & 6.2\% & 1.2\% & 4.1\% \\ \hline \hline \end{tabular} A is the architecture of 3D-2D-CNN-BGRU-CTC; B is the architecture of 3D-ResNet50-BGRU-CTC; C is the architecture of 3D-2D-CNN-TCN-CTC; D is the architecture of 3D-ResNet50-TCN-CTC.  
\end{table}
Table 4: Comparison between different architectures.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Seen(WER)** & **Unseen(WER)** & **Seen(CER)** & **Unseen(CER)** \\ \hline LipNet [22] & 4.8\% & 11.4\% & 1.9\% & 6.4\% \\ \hline WLAS [30] & 3\% & NA & NA & NA \\ \hline LCANet [23] & 2.9\% & NA & 1.3\% & NA \\ \hline
3D-2D-CNN-BLSTM [24] & 1.3\% & 8.6\% & NA & NA \\ \hline Our model & 1.1\% & 6.2\% & 1.2\% & 4.1\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance on the GRID dataset.