esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion  
Previous studies have shown that with an increase in the number of dry-wet cycles, the number of cracks on the specimen surface increases, and the length and width of cracks also increase [14]. Therefore, before and after the sandstone ring is subjected to dry and wet cycling, the volume of the specimen increases after the dry and wet cycle, which represents  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Inside** & **Specimen** & **Incident** & **Reflected** & **Transmitted** & **Absorbed** \\
**Diameter** & **Number** & **Energy** & **Energy** & **Energy** & **Energy** \\
**/mm** & & **J** & **J** & **J** & **J** \\ \hline \multirow{3}{*}{0} & PJ06-01 & 133.70 & 61.27 & 20.76 & 51.67 \\  & PJ06-02 & 134.09 & 60.25 & 21.38 & 52.46 \\  & PJ06-03 & 131.87 & 62.61 & 19.95 & 49.31 \\ \hline \multirow{3}{*}{5} & PJ06-04 & 132.96 & 61.57 & 19.59 & 51.80 \\  & PJ06-05 & 131.82 & 61.09 & 19.27 & 51.46 \\  & PJ06-06 & 132.18 & 59.41 & 20.46 & 52.31 \\ \hline \multirow{3}{*}{10} & PJ06-07 & 131.48 & 67.53 & 15.31 & 48.64 \\  & PJ06-08 & 129.35 & 62.05 & 17.07 & 50.23 \\  & PJ06-09 & 130.47 & 64.56 & 16.80 & 49.11 \\ \hline \multirow{3}{*}{15} & PJ06-10 & 131.24 & 69.08 & 12.87 & 49.29 \\  & PJ06-11 & 128.09 & 67.20 & 11.92 & 48.97 \\  & PJ06-12 & 127.37 & 69.66 & 10.32 & 46.39 \\ \hline \multirow{3}{*}{20} & PJ06-13 & 131.26 & 75.95 & 9.34 & 45.97 \\  & PJ06-14 & 135.07 & 74.39 & 9.81 & 49.87 \\  & PJ06-15 & 133.53 & 75.62 & 10.55 & 47.36 \\ \hline \multirow{3}{*}{25} & PJ06-16 & 127.17 & 77.22 & 6.30 & 43.65 \\  & PJ06-17 & 136.77 & 82.81 & 7.88 & 46.08 \\ \cline{1-1}  & PJ06-18 & 132.52 & 78.56 & 7.62 & 46.34 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Energy variation in the ring sandstone specimen.  
Figure 8: Diagram of dissipated energy per unit volume with inner diameter.  
the volume expansion of the specimen caused by the increase in the number of cracks and expansion after the dry and wet cycle. Under the action of a dynamic splitting load [22], the sandstone ring specimen will not fail with the increase in inner diameter and the decrease in wall thickness. Compared with this test, with the increase in inner diameter and the decrease in wall thickness, the sandstone ring specimen is prone to instability failure under impact compression load.
# A Method of Discriminating Between Power Swings and Faults Based on Principal Component Analysis
## 3 Power Swing Blocking Method Using PCA
### Flow Chart of PSB and PSD Function  
The flow chart of the PSB function, using the criterion mentioned in Section 3.2., is shown in Figure 4. As shown in the figure, the starting unit uses the instantaneous value of three-phase current to judge the starting time. The criterion of starting unit is as follows:  
\[\left|i_{\psi}\right|>I_{\rm set} \tag{22}\]  
where \(\psi=A,B,C\). As Figure 4 shows, after the starting unit operates, the value of \(C_{\rm avr}\) is calculated after a half-cycle delay. If (19) is satisfied, the PSBU blocks the protection relay. After blocking the protection relay, the PSBU enters into the process of PSD. The flow chart of the PSD function is shown in Figure 5.  
Figure 3: The value of \(C\).  
Figure 4: Flow chart of blocking protection relay.  
Figure 5: Flow chart of deblocking the protection relay.  
As Figure 5 shows, after blocking the protection relay, the PSBU continues calculating \(P_{\rm UI}\) and \(\Delta P\) by using the three-phase voltage and current data. If (21) is not satisfied, the value of \(P_{\rm UI}\) at this moment is saved for the next calculation. If (21) is satisfied, the PSBU deblocks the protection relay. Otherwise, the value of \(P_{\rm UI}\) at this moment is saved for the next calculation.
# Laser-Ultrasound-Based Grain Size Gauge for the Hot Strip Mill
## 1 Introduction
### Grain-Size Measurement with Laser Ultrasonics
For the actual grain-size calculations, only the third-order frequency-dependent attenuation \(b\) is considered, and the first parameter \(a\) is disregarded. For convenience, the average grain size can be expressed as  
\[\overline{D}=\Gamma(T)b^{\prime}, \tag{5}\]  
where \(b^{\prime}=\sqrt{1000b}\) and \(\Gamma(T)=\sqrt{\frac{1}{K(T)}}\) is a transfer function from attenuation to grain size containing the material- and temperature-dependent factors, such as anisotropy/texture. The challenge of calculating the grain size from laser ultrasonic measurements consists of knowing the transfer function, i.e., the calibration curve, which can be obtained theoretically or, as in the present paper, experimentally, by comparing the measured attenuation on several samples with accompanying microscopy for the determination of the actual grain size.
# Statistical Analysis of Cutting Force and Vibration in Turning X5 CrNi18-10 Steel
## 3 Results
### Statistical Analysis  
Statistical analysis consists of the correlation of the results between the feed, active vibration RMS values, and active cutting force RMS values. In addition, the analysis of variance checks the validity of the model. The linear regression line graph between the feed, active vibration RMS, and active force RMS is shown to provide insights. To conclude the effect of the machining parameter on the active vibration and active force, an estimated marginal means plot is drawn.  
Table 4 represents the calculated data of the active cutting force RMS and the active vibration RMS data for correlation with the machining parameters, feed rate (_f_), depth of cut (a\({}_{\text{p}}\)), and cutting speed (V\({}_{\text{c}}\)). To understand the relationship between the variables, Pearson correlation analysis was studied, where the correlation coefficient lies between \(-1\) and \(1\). In the case of the coefficient of relation, \(r\) > 0 shows a positive correlation between the  
two variables, the value of r is near +1 means the correlation is a strong positive correlation, and when the value is near 0, it shows the correlation is weak. If r < 0, there is a negative correlation between the variables, which shows that changing the value of the variable will not have a direct effect on the other.  
The correlation coefficient between the feed rate and active force RMS is 0.688, indicating a very strong positive linear relationship which suggests that increasing the feed rate also influences the increase in the active force, and there is less than 1% probability that this observed correlation is due to random variation.  
The correlation between the feed and active vibration RMS also suggests a strong positive linear relationship with a correlation coefficient of 0.697; as the feed rate increases,  
Figure 8: The standard deviation of active cutting force magnitude and active vibration magnitude.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Correlations Variables** & **Feed Rate (\(f\))** & **Active Vibration RMS** & **Active Force RMS** \\  & & **(AV\({}_{\text{b}}\) RMS)** & **(AF RMS)** \\ \hline Feed Rate (\(f\)) & 1 & 0.697 ** & 0.688 ** \\ Active Vibration RMS & & 1 & 0.699 ** \\ (AV\({}_{\text{b}}\) RMS) & & 1 & 0.699 ** \\ Active Force RMS & & 0.688 ** & 0.699 ** & 1 \\ \hline \hline \end{tabular}  
* Correlation is significant at the 0.01 level (2-tailed).  
\end{table}
Table 5: Correlation analysis feed, active force RMS and active vibration RMS.  
the active vibration RMS also increases. The correlation coefficient between the active force RMS and active vibration RMS is 0.699, which is statistically significant at the 0.01 level, suggesting that a higher active force influences a higher active vibration.
# A Data Augmentation Method for Skeleton-Based Action Recognition with Relative Features
## 5 Conclusions  
In this paper, we proposed a novel human action recognition approach in which skeleton sequences are transformed into color motion images, and a shallow CNN model is introduced for action classification. For each action sequence, three relative features with informative spatial-temporal information are calculated to depict human motions. Next, these features are normalized and transformed into color images. The motion image contains the spatial relations of human skeleton joints, and abundant dynamic information is provided to represent an action. Finally, a shallow CNN model is adopted to extract the deep features of the generated motion images and classify actions. The experimental results show that our model has superior efficiency and a high capacity. The research contributions of our method can be summarized in three points. First, a novel relative coordinate not only depicts the spatial relations of human skeleton joints but also provides long-term temporal information, is introduced in this paper. Moreover, the relative feature is viewpoint invariant and has more general applicability. Second, an effective skeleton sequence refinement strategy is adopted to cope with varying-length action sequences. Third, a data augmentation strategy is proposed to provide action samples and prevent CNN models from overfitting when training on a few samples. Experimental results prove that our data augmentation methods can effectively solve the overfitting problem and can be widely adopted in skeleton-based HAR methods.  
Figure 8: Five key poses of two similar actions in the Florence-3D. (**a**) Drink from bottom. (**b**) Answer phone.  
Figure 7: The recognition accuracy of each action on UTkinect-3D dataset using our method.  
In our future work, we will focus on more efficient relative features and encoding techniques for transforming action sequences into color motion images. In addition, we would like to detect external object information related to human poses and encode them into images as a feature for performance improvements.  
J.C.: conceptualization, methodology, software, writing--original draft. W.Y.: supervision, writing--reviewing and editing. C.L.: supervision, reviewing, and editing. L.Y.: supervision, reviewing, and editing. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Special Project 03 of Jiangxi Provincial Department of Science and Technology under Grant 20212 ABC03A36, by the Scientific and Technological Projects of the Nanchang Science and Technology Bureau under Grant GJJ202010, GJJ202017, GJJ191004 and by the National Natural Science Foundation of China under Grant 61862043.  
Not applicable.  
The authors declare no conflict of interest.
## 1 Introduction
In this case, taking into account the entire model in Figure 1a is an unnecessary burden as only activities A, G and H are of interest. Therefore, before launching the analysis (either visually by a human or by any automatic techniques), it will be convenient to get a simplified model containing only the desired activities, like the model shown in Figure 1b. In this example, not only the unneeded activities have been eliminated. The resulting model includes fewer Exclusive Gateways and fewer paths (sequence flows). In short, a new version of the entire workflow has been generated, but it was preserved its original behaviour (taking into account only those activities of interest). Despite the simplicity of this example, the complexity of this transformation is elevated for a real world BPMN model. As the reader may note, if an activity or set of them do not provide any actual information, then removing it can be a real advantage. The simplification is clear in a more compact model: the fewer elements involved, the easier it is to understand the model. Of course, the resulting model can be tackled in a more efficient fashion in further analysis and operations. Analysis of the simplified workflow (including only the needed behaviour) can produce even more transparent and concise results due to the elimination of unnecessary elements. In this way, it is possible to have a general model with hundreds of activities and generate different perspectives depending on the particular interests. With this, we will have access to a complete family of processes. This type of simplification attains greater significance in real world process models. In an organisation with hundreds of models containing hundreds of activities, it is possible to find superfluous or repetitive paths that do not provide new information to the behaviour of the model. This work presents an automatic heuristic-based approach to perform this type of simplification on any given BPMN model. Starting with a model (like in Figure 0(a)) and a list of the interesting activities, the objective is to achieve a simpler model, as presented in Figure 0(b), containing just the relevant information for a specific goal. To get this result, several issues must be borne in mind as discussed in following sections. The remainder of this paper is structured as follows. Next section highlights other approaches available in the literature related to this work. Then, Section 3 discusses about the pattern repository, a main piece of the proposed algorithm. Section 4 deeply describes the algorithm devised. Section 5 performs a discussion about the complexity of the algorithm. Afterwards, Section 6 presents the algorithm validation using 8102 cases taken from real world examples. Section 7 verifies the usability of the proposal in a real scenario. Finally, Section 8 points out the conclusions.
## 6 Authentication with Web 3.0
### System Components
#### 6.2.3 Back-End  
The back-end application should be secure and capable of recovering the message signed using the elliptic curve algorithm.  
The elliptic curve digital signature algorithm (ECDSA) [18] is a variant of the digital signature algorithm (DSA) which uses elliptic curve cryptography.  
Elliptic curve cryptography (ECC) [19] is a public key encryption algorithm based on elliptic curve mathematics. The main advantage of ECC is that it uses a smaller key length and provides a comparable level of security compared to the Rivest-Shamir-Adleman (RSA) encryption algorithm. ECDSA is a combination of ECC and DSA (digital signature algorithm).  
Compared with RSA, the public key length of ECDSA is shorter, and the encrypted message will be smaller, so the computation and processing time will be shorter, and the memory and bandwidth requirements will be smaller.  
**Generation phase:**  
Select Ep(a,b), x, and \(1\leq x<n\).  
Select G \(\epsilon\) Ep(a,b) with order n and compute Q = dG  
Public key: (Ep(a,b), p,G,n,Q)  
Private Key: x  
**Signature Algorithm**  
Select k, \(1\leq\) k < q.  
kG = (x1,y1), r = x1 (mod n)  
s = k \(-\) 1 (H(m) + xr) (mod n)  
(r, s) is the Signature of m.  
**Verification Algorithm**  
w = s\({}^{-1}\) (mod n)  
u1 = H(m)w (mod n)  
u2 = rw (mod n)  
u1G + u2Q = (x2,y2),  
v = x2 (mod n)  
v = r \(\rightarrow\) accept the Signature [20].
## Materials and Methods
### Extraction of Spectral Data  
Initially, the differential GPS locations of each sampled tree were overlayed onto the WV2 and WV3 images (panchromatic band) using ArcGIS 10.2 (Environmental Systems Research Institute. Redlands, CA, USA). A 2 m radius buffer area was applied around each point (12.6 m\({}^{2}\) area) as the canopy radius of each sampled trees was greater than 2 m. This ensured that the extracted pixel values were specific to the selected tree canopies only and did not include any pixels influenced by shade or specific to inter rows. Using the open source software Starspan GUI [42] each area of interest (AOI) was used to subset the 8 band spectral information for each tree canopy from the non-pansharpened imagery. From the extracted data, 18 structural and pigment based VIs specific to crop biomass and yield parameters [35] were derived (Table 1) and regressed against total fruit weight (kg\(\cdot\)tree\({}^{-1}\)) and average fruit size (g\(\cdot\)tree\({}^{-1}\)). This was undertaken for all combined individual tree data collected over the three year study as well as for each block separately.  
Figure 2: Classified Normalised Difference Vegetation Index (NDVI) images of the five avocado blocks with sampling locations for the 2016 and 2017 season presented as black circles. In Block 1 and 2, the individual tree sampling locations for 2014 are indicated as white circles. (Note some sample locations overlap between years.) Block 1: 14.4 ha, Block 2: 28.2 ha, Block 3: 6.8 ha, Block 4: 14.7 ha, Block 5: 12.7 ha and Block 6: 11.3 ha.
## 2 Materials and Methods
### SPECT Acquisition and CVR Processing  
\({}^{99\text{m}}\)Tc-HMPAO and one-day protocol were used for SPECT imaging (Figure 4). The one-day protocol allows both pre- and post-vasodilation scans to be completed within a single day, offering convenience for patients by completing both air and 5% CO\({}_{2}\) inhalation scans in one visit [10]. For the baseline SPECT scan, participants received an injection of 20 mCi of \({}^{99\text{m}}\)Tc-HMPAO ten minutes before the start of the scan. A 15 min SPECT scan was then performed while the participant inhaled room air. Following this baseline scan, a 5% CO\({}_{2}\) gas delivery system was used to administer CO\({}_{2}\) for 15 min to induce cerebrovascular stress. Five minutes after the start of the baseline SPECT scan and during this CO\({}_{2}\) inhalation period, a second injection of 40 mCi of \({}^{99\text{m}}\)Tc-HMPAO was administered  
Figure 3: Representative brain sagittal images with the levels of activation during inhalation of 5% CO\({}_{2}\), which was identified by statistical parametric mapping (SPM) first level analysis.  
Figure 2: A pipeline of data processing steps for the cerebrovascular reactivity (CVR) mapping method. AAL2, automated anatomical labeling atlas; BOLD, blood oxygen level-dependent; FWHM, full width at half maximum; ROI, region of interest; SPECT, single photon emission computed tomography.  
to acquire stress images. This second dose of the radioisotope was allowed to be absorbed by the dilated brain tissue for 10 min while CO2 inhalation continued. Subsequently, a 15 min SPECT scan was conducted to obtain the stress images reflecting the increased cerebral blood flow due to CO2.  
SPECT images were obtained using a dual headed rotating gamma camera (Symbia T16; Siemens, Erlangen, Germany) equipped with a low energy, high resolution collimator (LEHR). During acquisition, a total of 32 views were collected with a 128 \(\times\) 128 matrix and a zoom factor of 1, using a step and shoot mode with 25 s per rotation. The total acquisition time was 55 min. The acquired SPECT images were then reconstructed using a 3D iterative method, followed by the application of a 6.6 mm Gaussian filter. Attenuation correction was performed using [PERSON]'s method [29]. Subsequent preprocessing was performed using SPM12 and in-house MATLAB code. This involved realignment to correct for motion, spatial normalization to the MNI space, and spatial smoothing with a 6 mm FWHM gaussian filter. During this SPM12 preprocessing pipeline, decay correction was applied to compensate for the radioisotope decay between the baseline and stress scans. Following SPM12 preprocessing, intensity normalization was performed using the cerebellum as a reference region to account for global signal differences [30; 31]. The resulting preprocessed CBF images were then used to calculate the CVR as in (2). Finally, the CVR values were mapped onto 120 ROIs defined by the AAL2 template.  
\[CVR=\frac{[SPECT\ (CO_{2})-SPECT\ (AIR)]}{SPECT\ (AIR)}\times 100\% \tag{2}\]
# An Approximation Algorithm for a Variant of Dominating Set Problem
###### Abstract  
In this paper, we consider a variant of dominating set problem, i.e., the total dominating set problem. Given an undirected graph \(G=(V,E)\), a subset of vertices \(T\subseteq V\) is called a total dominating set if every vertex in \(V\) is adjacent to at least one vertex in \(T\). Based on LP relaxation techniques, this paper gives a distributed approximation algorithm for the total dominating set problem in general graphs  
neighborhood of the vertex, where the _dynamic degree_ is the number of white vertices in the open neighborhood of a vertex at a given time. The communication model used in this paper is synchronous [15]. That is to say, in each communication round, every vertex can send a message to each of its neighbors in the given graph; the time complexity of the algorithm is to compute the number of communication rounds. In this paper, the number of communication rounds for our algorithm is a constant. Unlike the existing distributed algorithms, the algorithms in [16; 17] focus on special graphs (planar graphs without three or four cycles), while this paper is concerned with general graphs. The self-stabilizing distributed algorithm designed in [13] can obtain a minimal total dominating set in polynomial time \(O(mn)\) (\(m\) is the number of edges in the graph and \(n\) is the number of vertices in the graph) in general graphs. Comparatively, the distributed algorithm designed in this paper, under a synchronous communication model, can obtain a non-trivial approximation ratio in general graphs with a constant number of rounds. The time complexity of our algorithm is \(O(k^{2})\), where \(k\) is a positive integer.  
The paper is organized as follows. We first give some complexity and algorithmic results on the TDS problem in Section 2. Section 3 introduces some necessary notations and presents the linear programming relaxation of the TDS problem. In Section 4, based on LP relaxation techniques and the definition of the maximum dynamic degree in the second neighborhood of the vertex, we design a distributed approximation algorithm for the TDS problem. In Section 5, we summarize this paper and introduce some future work.