esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Establishment of CPPS Real-Time Hardware-in-the-Loop Co-Simulation Platform
### Modeling and Simulation of Power Physical System  
The key to real-time power simulation is that the task can be decomposed by the distributing process and executed across a parallel infrastructure. Normal power system simulators are attached to a PC, which cannot satisfy the requirements of calculation speed. Considering the real-time requirements, there are two commercial specialized simulators to choose from: the Real-Time Digital Simulator (RTDS) (developed by RTDS Technologies, Winnipeg, MB, Canada) and RT-LAB (developed by OPAL-RT Technologies, Montreal, QC, Canada). In consideration of the relatively mature model library and cheap cost of RT-LAB, it was chosen as the real-time simulator of the power system.  
Through full integration with MATLAB/Simulink (developed by MathWorks, Natick, MA, USA). RT-LAB is compatible with entire models of MATLAB/Simulink. Moreover, RT-LAB is equipped with several special model libraries like RT-events, RT-Drive, and ARTEMIS, which cover various power system situations including the electromagnetic transient simulation of a large-scale Alternating Current/Direct Current (AC/DC) power system, power grid planning and feasibility analysis, transient stability simulation of power system, and an integration test of flexible AC transmission systems (FACTs) and high voltage direct current transmission (HVDC) control systems.  
RT-LAB provides the common models for interaction with exterior environments, which are as follows: the OpIPSocketCtrl module is responsible for data control, the OpAsyncRecv module is responsible for data reception, and the OpAsyncSend module is responsible for data sending. By employing these modules, RT-LAB can communicate with other simulators through the TCP/IP protocol.
## 3 Basic Statistical Analysis
### Correlation Analysis
#### 3.1.2 Kendall's Tau Coefficient  
Kendall's tau coefficient (\(\tau\)) is a nonparametric statistic that measures the ordinal association between two ranked variables, assessing the similarity of the rankings by counting concordant and discordant pairs of observations; it ranges from \(-1\) (perfect disagreement) to 1 (perfect agreement), with 0 indicating independence between the rankings [37]. Specifically, [PERSON]'s tau counts the number of paired observations that are in the same order (concordant) or different orders (discordant) in the two rankings, adjusting for ties, and bases a coefficient on this count to quantify agreement [38]. Because it makes no distributional assumptions, [PERSON]'s tau is often useful for non-normal data or ordinal variables where [PERSON]'s correlation may not apply [39]. Because it focuses on comparing rankings rather than a linear relationship, [PERSON]'s tau is useful for assessing agreement between ratings or rankings generated by qualitative human judgment [40]. For instance, compare each pair of observations at the monitoring point. A pair is concordant if the ranks of both the displacement and stability reduction factors move in the same direction (both increase or both decrease). It is discordant if they move in opposite directions. The general formula for [PERSON]'s tau is in Equation (3).  
\[\tau=\frac{\text{M}-\text{N}}{\frac{1}{2}\text{n}(\text{n}^{2}-1)} \tag{3}\]  
where  
\(\tau=\) reflects the balance between concordant and discordant pairs  
M = tally of the number of concordant pairs  
N = tally of the number of discordant pairs  
n = number of observations (or data points) at a monitoring point.
# Reverse Knowledge Distillation with Two Teachers for Industrial Defect Detection
## 4 Experiments
### Datasets  
MVTec AD is an industrial inspection-focused dataset for comparing anomaly detection methods. There are 15 different object and texture categories. Each class is split into a training set and a testing set. There are 3629 training images of typical objects and 1725 test images with various types of anomalies. For the test image, pixel-level binary annotations for anomalous images in the test set are provided.  
BTAD is a dataset of actual industrial anomalies. The datasets include 2830 images of 3 industrial products that display flaws on their bodies and their surfaces. Additionally, it is separated into 1800 training images and 741 test images. It contains 3 categories, two objects, and one texture. The test datasets only contained one type of anomaly.
## 3 System and Problem Definition for Scheduling Problem
### Problem Definition  
The problem addressed by the paper is the allocation of jobs to a finite set of resources such that value obtained from completion of the jobs is maximized while energy consumption is minimized simultaneously. The problem is defined as follows:  
* **Input:** Job queue \((j_{1},...,j_{n})\), Value curve for each job \(VC_{j}\), Nodes within the HPC data center \((N_{0},...,N_{i})\).
* **Constraints:** Restricted available cores on the nodes in \(HDC\).
* **Objective:** Jointly optimize overall value \(Val_{total}\) and energy consumption \(E_{total}\), by maximizing the quotient \(Val_{total}/E_{total}\).  
The RMS needs to make very fast decisions on which node to allocate a job and which cores within that node should do the computation. It is assumed that any one job requires a minimum of one core and individual cores are never shared between multiple jobs.
## 3 Proposed Method
### Attention Module
#### 3.3.2 Spatial Attention Module  
The authors of [36] point out that the merge operation in the channel dimension can highlight the feature map's information area. After the channel attention module, we introduce a spatial attention module to focus on where features are meaningful. Similar to channel attention, given the feature map \(F^{\prime}\) after the channel attention module, the spatial attention module first performs the average pooling and maximum pooling of the channel dimensions to obtain two \(H\times W\times 1\) channel descriptions and stitch these two descriptions together according to the channels. Then, the weight coefficient \(M_{S}\) is obtained after a \(7\times 7\) convolutional layer, and the activation function is Sigmoid. The spatial attention module is shown in Figure 5b and is computed as:  
\[M_{\text{s}}(F^{\prime})=\sigma(Conv_{7\times 7}([AvgPool(F^{\prime}),MaxPool(F^{ \prime})])) \tag{2}\]  
Figure 6 shows the structure of the attention module used in SSD7-FFAM, where \(F^{\prime}\) is the new feature obtained by multiplying \(M_{c}\) and the input feature, and \(F^{\prime\prime}\) is the result of multiplying \(M_{S}\) by \(F^{\prime}\). The final feature map \(F_{f}\) is the result of adding \(F\) and \(F^{\prime\prime}\) to the ReLU activation function.  
\[F^{\prime}=F\times M_{c}(F) \tag{3}\]  
\[F^{\prime\prime}=F^{\prime}\times M_{\text{s}}(F^{\prime}) \tag{4}\]  
\[F_{f}=\sigma(F+F^{\prime\prime}) \tag{5}\]
## 3 Results and Discussion
### Effect of VPD Regulation on Tomato Crops in Soil above PVC Trays
#### 3.2.1 Effect of VPD Regulation on Growth  
The results in Figure 6 show the effects of VPD regulation on crops developed in trays with soil, i.e., experimental treatments T1 DS and T2 DS. As depicted in Figure 6a, the crop height of T2 DS increased progressively from 23 days after transplanting the crops, whereas the height of the crops of T1 DS did not increase at the third moment of the crop cycle: 36 days after transplanting the crops. T1 DS performed slightly better in crop height at the second moment of the crop cycle: 23 days after transplanting the crops.  
Figure 5: _Cont._  
In terms of the evolution of leaves developed, both treatments had similar patterns to the crop height observations, as described in Figure 6b. In two growth parameters, namely, crop height and leaves developed, observed 36 days after transplanting, the response to treatment T2 DS was better than the response to treatment T1 DS. As for the fruit setting of Figure 6c, T2 DS showed better results than T1 DS at all moments of the crop cycle.  
Figure 6d shows that the phenological growth increased from 49 days after the transplant. There were no differences between treatments in the phenological growth stages between the two treatments, as the standard error bars show.  
Regarding H1 in this group, the results measured for the four growth parameters had a normal distribution, and the \(p\)-values of the \(t\)-tests were greater than 0.05, so there were no statistically significant differences between the two treatments. H1 for the growth parameters in this group is not true.
# Cross-View Attention Interaction Fusion Algorithm for Stereo Super-Resolution
## 2 Network Architecture  
The left and right images with low resolution are input into the existing basic single image super-resolution network branches for training, and the pre-trained network model is obtained. The whole network model of stereo super-resolution mainly includes three parts: single image super-resolution branch, attention stereo fusion module and enhanced cross-view interaction.
## 5 Conclusions  
1. In the analysis of non-ponding rainfall infiltration, the model proposed in this paper is more in line with the distribution of moisture content in the actual infiltration process than the traditional model, and better able to reveal the variation trend of soil surface moisture content and wetting front depth with rainfall time.
2. Under the two hypotheses of uniform moisture content and non-uniform moisture content, before the critical moisture content, to reach the same surface moisture content, the time required for the former is longer than the latter if the rainfall intensity is small. When the rainfall intensity increases to a certain extent, the time difference does not change significantly.
3. Due to the variation in moisture content over time, the moisture content calculated by the proposed model is lower than that of the traditional Mein-Larson model under the same rain intensity and duration, but the wetting front depth is significantly higher than the latter, which has a wider scope of influence on soil moisture content, indicating that long-term unsaturated infiltration can affect the moisture content of deeper soil, thus causing slope instability.  
Conceptualization, M.Y. and T.C.; software, M.Y. and X.W.; validation, M.Y. and X.W.; investigation, W.T.; data curation, X.W. and R.F.; writing--original draft preparation, M.Y.; writing--review and editing, M.Y. and T.C.; visualization, W.T. and X.W.; supervision, J.L.; project administration, R.F. and J.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by \"Science and Technology Research and Development Major Program of China Railway Corporation, grant number K2019G042\" and \"The Fundamental Research Funds for the Central Universities, grant number 2021 YJS114\".  
Not applicable.  
Figure 13: Seepage mode when the critical moisture content is less than the initial moisture content: (**a**) Uniform moisture content; (**b**) Non-uniform moisture content.  
Informed Consent Statement: Not applicable.  
Data Availability Statement: Data sharing not applicable.  
The author would like to thank all the anonymous reviewers for their meticulous comments and helpful suggestions.  
The authors declare no conflict of interest.
## 4 Discussion
### Results of the Effect of Gravity  
For the effect of gravity on the imaging quality of mirror surface shape accuracy in assembly, both the experimental and simulation results show that the imaging quality is poorer in the three-hanging-point method than in the six-hanging-point method, which proves that the six-hanging-point method is more suitable for a grazing incidence focusing mirror suspension of this diameter than the three-hanging-point method that causes less change in the surface shape. However, the experimental results in both ways are more significant than the simulation results, mainly due to the following:  
(1) The position of the hanging plates has an influence; although the upper motion module is arranged uniformly along the circumferential direction, the hanging plates underneath are bonded to the outer surface of the mirror due to the rotatability of the hanging plates. There are minor differences in the positions of the hanging plates bonded to the outer surface of the mirror, which influences the uniformity of the hanging points arranged on top.  
(2) The spatial position of the mirror is precisely adjusted by the piezoelectric actuator so that the imaging quality of the focused spot reaches the most superior state. However, in this process, due to the slight difference between the optical axis of the mirror with surface shape error and the standard optical axis position of the ideal mirror, the spatial attitude of the mirror adjusted in the experiment is slightly different from the ideal position  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Assembly Process** & **Simulation Results** & **Experimental Results** \\ \hline During assembly & 51.37\({}^{\prime\prime}\) & 56.76\({}^{\prime\prime}\) \\ (three-hanging-point) & 54.65\({}^{\prime\prime}\) & 54.65\({}^{\prime\prime}\) \\ (six-hanging-point) & 52.23\({}^{\prime\prime}\) & 58.86\({}^{\prime\prime}\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of image quality of focusing mirror by angular resolution.  
Figure 17: (a) Imaging quality after 24 h of adhesive layer curing; (b) Angular resolution.  
of the absolute perpendicularity, which leads to the changes in the imaging state of the focused spot.
## 1 Introduction  
Terra rossa (lateritic weathering crusts) surfaces on carbonate rocks occupy an important position in karst environmental systems research [1]. These weathering crusts are regarded as an area of geochemical sensitivity and ecological vulnerability [2]. Carbonate rocks, covering approximately 15% (22 million km\({}^{2}\)) of the world's total land area, are mainly distributed in tropical and subtropical areas, such as Southeast Asia, the Mediterranean coast, and the Southeastern region of North America [3,4]. Southeast Asia is the largest contiguous karst area on Earth [5,6]. Showing strong effects of geochemistry and pedogenesis, the karst geology, geomorphology, and lateritic weathering crusts were formed as unique geological bodies compared with non-karst regions [7].  
After multi-stage development, terra rossa recorded information about paleoenvironment and paleoclimatic changes [8,9]. This information is vital for paleoclimate analysis, especially in the karst regions that lack Cenozoic sediments [10,11]. In Southeast Asia, extensive amounts of lateritic weathering crusts overlaying carbonate rocks formed an intact area of terra rossa distribution within the tropical-subtropical monsoon climatic zones. The regions thus became ideal for the formation and evolution of research on lateritic and carbonate rock weathering. Some studies focused mostly on subtropical [12, 13, 14] and Mediterranean regions [15, 16, 17]; few focused on the tropical karst areas. Although [PERSON] and [PERSON] [18] researched the sources of laterite in the tropical West Indies region, they did not conduct in-depth investigations on the lateritic geochemical evolutionary processes.  
Rare earth elements (REEs) serve as tracers and indicators of sources, weathering, diagenesis, and mineralization processes in the terrestrial critical zone [19]. Their contents, total amount, and combinational patterns objectively reflect the geological body's evolutionary process, the physical and chemical conditions, as well as the source [20,21]. As such, studies of the geochemical behavior of REEs in terra rossa can contribute to reconstructing the pedogenesis effects, weathering processes, and the supergene mineralization [22, 23, 24].  
In this study, we selected two typical terra rossa profiles, covering dolomite and limestone, to investigate the evolutionary process of REEs in the tropical carbonate lateritic crusts to reveal their implications for environmental changes, geochemical characteristics, and elemental migration.
# HSAW: A Half-Face Self-Attention Weighted Approach for Facial Expression Recognition
## 3 Analysis of the Asymmetry of Facial Expressions
### Statistical Analysis
#### 3.1.4 Results of the Analysis  
The results of the statistical analyses of human reaction time (RT) during recognition of half-face expressions, ratings of the intensity of half-face expressions, and the accuracy of half-face expression categorization are as follows.  
An independent-sample \(t\)-test revealed no significant differences in RT between the left half-face (M = 1.461, SD = 1.292) and right half-face (M = 1.518, SD = 1.607), where \(t_{(902.606)}=-0.619\), \(p=0.536>0.05\). Furthermore, we classified the emotions correctly recognized by the subjects into positive and negative emotions, with positive emotions only containing happiness and negative emotions including fear, nausea, sadness, and anger. The purpose of doing this is to explore whether positive and negative emotions will have a significant impact on the reaction time of subjects. An independent-sample \(t\)-test revealed significant differences in RT between the positive emotions (M = 0.766, SD = 0.563) and the negative emotions (M = 1.863, SD = 1.516), \(t_{(\mathrm{S21})}=-11.686\), \(p<0.001\). These results indicate that the RT of positive emotions is significantly less than the RT of negative emotions.  
An independent-sample \(t\)-test for facial emotional intensity revealed a significant difference between the left half-face (M = 3.25, SD = 1.279) and right half-face (M = 2.91, SD = 1.256), \(t_{(\mathrm{S217},\mathrm{154})}=6.773\), \(p<0.001\). The above results prove that the emotional intensity of the left half-face is significantly greater than that of the right half-face.  
A Chi-square test revealed significant differences in accuracy of discrimination of all emotion types between the left half-face and right half-face, [PERSON] \(\chi^{2}(1)=10.552\), \(p=0.001\). The result revealed that the accuracy of the left half-face is significantly higher than that of the right half-face. In addition, we also divided seven emotion types into positive and negative emotions and conducted a Chi-square test on each of them. The results indicate that the accuracy of discrimination positive emotions is still significantly higher on the left face than on the right face, [PERSON] \(\chi^{2}(1)=13.425\), \(p<0.001\). However, the accuracy of identifying negative emotions has no significant differences on the left half-face and right half-face, [PERSON] \(\chi^{2}(1)=3.318\), \(p=0.069>0.05\).  
The present Section 3.1 focuses on whether a person observing only the left half of the face and the right half-face affects facial expression recognition. The results showed that the emotional intensity of the left half-face was significantly higher than that of the right half-face, which demonstrated the existence of half-face asymmetry in facial expressions.
# Benchmarking Deep Learning Methods for Aspect Level Sentiment Classification
## 3 Deep Learning Methods for Aspect Level Sentiment Classification
### Data Modelling Procedure for Deep Learning-Based ALSC  
In ALSC, it is desired to detect the polarity of aspects contained in a sentence. A sentence with aspects is transformed into machine-readable vector form as an input to a deep learning network. This input to the network varies according to the architecture of the deep learning method. Table 1 provides a brief description of various deep learning methods along with the inputs required by them. To understand the different types of inputs, consider a sentence S followed by an Aspect A as given below:  
\"But the wine list is excellent\". **Sentence** S is a sequence of words {\(\text{w}_{1},\ \text{w}_{2}\ldots\text{w}_{n}\)}. In the above example S will be denoted as {'but', 'the', 'wine', 'list', 'is', 'excellent'}  
**An Aspect** that may consist of one or more than one word, is a subsequence of a sentence and is denoted by A. In the above example, Aspect A is denoted by {\"wine list\"}  
**Context** C is the part of a sentence other than aspect. For, e.g., in above sentence, C is {'but', 'is', 'the', 'excellent'}  
**C\({}_{\text{i}}\) and C\({}_{\text{r}}\) are left and right context**, respectively. For above, e.g., C\({}_{\text{i}}\) is {'but', 'is'}: and C\({}_{\text{r}}\) is:{'the', 'excellent'}  
**Dependency Tree** D\({}_{\text{T}}\) is a directed graph showing the relationship between different words of a sentence. The dependency tree for the above sentence is shown in Figure 5.  
Figure 4: Deep Learning Methods for ALSC.
## 6 Conclusions and Future Works
### Model Selection Guidelines for Practical Application  
Based on the comparative results and model characteristics, the following guidelines are proposed to assist practitioners in selecting the most appropriate model for specific disaster response scenarios:  
* Overlay-Based Approach (Approach 1): This method is computationally light and offers the fastest inference speed. It is particularly suitable for rapid situational awareness in the early phase of a disaster, where quick mapping of the road network is more important than precise damage identification.
* Difference-Based Approach (Approach 2): This model offers the best overall balance between performance and stability among the evaluated approaches. While its low detection recall for damaged segments remains a significant limitation, its superior overall F1-score (\(F1_{s}\)) makes it the most practical choice for generating a first-pass assessment in detailed damage analysis. It is best used for prioritizing areas for on-site inspection rather than as a definitive tool for damage identification.
* Siamese-Based Approach (Approach 3): This model demonstrated a high-precision, low-recall profile, making it suitable for applications where ensuring high precision is the top priority. However, due to its heavier computational requirements and performance instability, it is less efficient for time-sensitive damage assessment.  
This operational guideline suggests that the difference-based model is optimal for real-world road infrastructure damage detection tasks, especially when considering the balance between inference time and segmentation accuracy.
## 6 Installation Error Compensation for the Shearer's SINS
### Influence of Installation Deviation Angle Error  
(1)  Accelerometer error caused by the installation deviation angle  
If the output value of the accelerometer in the \(b\) system is \(f_{ib}^{b}\), the output value of the accelerometer in the \(f\) system is  
\[f^{f}=C_{b}^{f}f_{ib}^{b} \tag{31}\]  
where \(C_{b}^{f}\) is the attitude matrix from the \(b\) system to the \(f\) system; then, the acceleration error in the \(f\) system is  
\[\delta f^{f}=f_{ib}^{b}-f^{f}=\Big{(}I-C_{b}^{f}\Big{)}f_{ib}^{b} \tag{32}\]  
Then, the acceleration error in the navigation coordinate system (\(n\) system) is  
\[\delta f^{n}=C_{f}^{n}\delta f^{f}=C_{f}^{n}\Big{(}I-C_{b}^{f}\Big{)}f_{ib}^{b} \tag{33}\]  
where \(C_{f}^{n}\) is the attitude matrix from the \(f\) system to the \(n\) system, and \(I\) is a unit matrix.  
(2)  Angular velocity error caused by the installation deviation angle  
The derivation process is the same as above; then,  
\[\delta\omega_{ib}^{n}=C_{f}^{n}(I-C_{b}^{f})\omega_{ib}^{n}, \tag{34}\]  
Figure 10: Pitch deviation angle of SINS.  
Figure 9: Roll deviation angle of SINS.  
where \(\delta\omega_{ib}^{n}\) is the measurement error of the gyroscope caused by installation deviation angle, and \(\omega_{ib}^{b}\) is the ideal output value of the gyroscope.