esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion and Conclusions  
### Discussion  
Structural health monitoring is a prerequisite and indispensable to safety monitoring and hazard early warning of offshore platform, and the SHM system needs to be designed before an offshore platform is put into operation. This paper designed a complete SHM system and sensor layout scheme for offshore platform, so as to provide a basis for subsequent SHM system installation and implementation. The design scheme and theoretical basis of the SHM system as well as the assessment subsystem was described in detail. The innovation of this study lies in the selection and layout scheme of sensors for an offshore platform under construction combined with the application strategy of monitoring data, which can provide reference for the subsequent installation and implementation of SHM system.  
As to the assessment of SHM system, actual monitoring data could make sense, but before the SHM system is put into operation, 3-D numerical simulation is usually employed to investigate its characteristics and performance under variety scenarios [32; 44]. So, in this paper, offshore platform in East China Sea was selected to establish the numerical model, the application and performance of the proposed SHM system were illustrated based on numerical simulation. The factors affecting the health of an offshore platform structure were also analyzed. The results show that the designed SHM system is reliable theoretically and technically. Currently, the SHM system has not yet been installed, but our study can guide the selection and construction of SHM system for the offshore platform. Taking the offshore platform in East China Sea as a case study, the application and performance of the SHM system have been illustrated, and it would be further verified after monitoring data are obtained.
## 5 Proposed Hardware Architecture
### Arithmetic Logic Unit
#### 5.5.1 Montgomery Radix-2 Multiplier  
The Montgomery multiplication method is a technique that replaces expensive division operations with simpler shift and addition operations, resulting in faster modular arithmetic computations. The radix-2 Montgomery method is a basic implementation of this method, as shown in Algorithm 2. The loop in the algorithm iterates over each element in the input vectors \(Xp\) and \(Yp\), and computes a partial product \(Xp_{i}\times Yp\) for each element. The partial product is added to the accumulator A in step 1.  
\(A[0]\) refers to the least significant bit of the accumulator A. In step 2 of the algorithm, the value of \(A[0]\) is used to determine whether to add the modulus \(p\) or not. If \(A[0]\) is 1 (i.e., the result of the addition operation in step 1 is odd), then \(p\) is added to A in step 3 to make it even. If \(A[0]\) is 0 (i.e., the result of the addition operation in step 1 is even), then adding \(p\) to A in step 3 has no effect on the value of A, and the algorithm proceeds to step 4 to shift A right by one bit. The result \(Zp\) is then returned. By replacing the division operation with shift and addition operations, the radix-2 Montgomery method results in a faster modular multiplication algorithm.  
Note that the input parameters \(p\), \(Xp\), and \(Yp\) are preprocessed to satisfy certain conditions, which are necessary for the correctness of the algorithm. Specifically, \(p\) is expressed in the form \(\sum_{i=0}^{n-1}p_{i}^{2}\), where \(p_{i}\) are binary digits, \(Xp\) and \(Yp\) are less than \(p\), and \(R\) is equal to \(2^{n}\). These preprocessing steps are not explicitly shown in Algorithm 2.
###### Abstract  
Large-scale and long-term landslide susceptibility assessments are crucial for revealing the patterns of landslide risk variation and for guiding the formulation of disaster prevention and mitigation policies at the national level. This study, through the establishment of a global dynamic landslide susceptibility model, uses the multi-dimensional analysis strategy and studies the development trend of China's large-scale landslide susceptibility. First, a global landslide dataset consisting of 8023 large-scale landslide events triggered by rainfall and earthquakes between 2001 and 2020 was constructed based on the GEE (Google Earth Engine) platform. Secondly, a global dynamic landslide susceptibility model was developed using the ResNet18 (18-layer residual neural network) DL (deep learning) framework, incorporating both dynamic and static LCFs (landside conditioning factors). The model was utilized to generate sequential large-scale landslide susceptibility maps for China from 2001 to 2022. Finally, the MK (Mann-Kendall) test was used to investigate the change trends in the large-scale landslide susceptibility of China. The results of the study are as follows. (1) The ResNet18 model outperformed SVMs (support vector machines) and CNNs (convolutional neural networks), with an AUC value of 0.9362. (2) SHAP (Shapley Additive Explanations) analyses revealed that precipitation played an important factor in the occurrence of landslides in China. In addition, profile curvature, NDVI, and distance to faults are thought to have a significant impact on landslide susceptibility. (3) The large-scale landslide susceptibility trends in China are complex and varied. Particular emphasis should be placed on Southwest China, including Chongqing, Guizhou, and Sichuan, which exhibit high landslide susceptibility and notable upward trends, and also consider Northwest China, including Shaanxi and Shanxi, which have high susceptibility but decreasing trends. These results provide valuable insights for disaster prevention and mitigation in China.  
dynamic landslide susceptibility; ResNet18; Mann-Kendall test; landslide conditioning factors +
Footnote †: journal: Applied sciences  
Article
# Tensegrity Applications to Architecture, Engineering and Robotics: A Review
## 3 Space Applications
### Masts  
As previously mentioned in the section Towers, [PERSON] described the construction of tensegrity structures by simple modules in his patent \"Continuous tension, discontinuous compression structures\" [23]. One of the structures he defined is a three-strut module mast. This mast is created by assembling triangular prisms (Simplex) on top of each other. [PERSON] [63] was a pioneer in studying foldable/deployable tensegrities, introducing the fundamental geometrical characteristics for deployment of tensegrity masts and some conceptual considerations for the one-dimensional deployable tensegrity.  
Following [PERSON]'s configuration, [PERSON] [62] proposed the four-stage mast, which was the third tensegrity mast built at DSL (Deployable Structures Laboratory) (Figure 19a).  
Figure 18: (**a**) Wind-adaptive element. Reprinted/adapted with permission from Ref. [60]. (**b**) TABS module, D-bar element and WTABS system [61] (Image courtesy of [PERSON]).  
With bi-stable struts, composed by four Simplex, it was the first one that could be folded in a satisfactory way.  
[PERSON] and [PERSON] [64] studied different masts that had been previously proposed, in order to find one suitable for deployment. They ended up deploying an eight-stage mast, as shown in Figure 19b. The major problem they found was that \"the mast had no stiffness during deployment. It might be possible to provide full stiffness to the deployed part through a special canister, which would have the role of the struts and prestress the cables\".  
The current state of the art is the primary deployable structure for the STRM (Shuttle Radar Topography Mission), consisting of the 60-metre-long ADAM (Able Deployable Articulated Mast). At the end of its length, the ADAM can support an antenna weighing 360 kg and can also carry electric and fiber-optic cables weighing a couple hundred kilograms, as well as a gas line. [62].  
Very recently, some authors have tried to answer the question about which tensegrity column would be the best for the purpose of deployable tensegrity columns [65]. They focus the research on four tensegrity columns (in various deployment configurations), some of them are included in Figure 2. The methodology was based on computer simulations with the finite element analysis (FEA) and multibody dynamics (MBD) software to provide quantitative and qualitative results.  
Figure 19: Examples of deployable tensegrity masts: (**a**) Deployment of the four-stage mast [62]. (**b**) Deployment of the eight-stage mast [64]. (Images courtesy of [PERSON]).
# Dual-Indicator Micro-Electro-Mechanical System Monitoring Method for Rock Instability Early Warning
## 3 Experimental Validation and Performance Evaluation
### Experimental Results
#### 3.2.2 Correlation Analysis of Stability and NF  
Following the described methodology, MEMS sensors acquired vibration curves from both bedrock and unstable rock masses, enabling the generation of frequency response function (FRF) curves across varying working conditions. The NF was derived by identifying the dominant peak within these FRF curves. As illustrated in Figure 8, the six FRF curves corresponding to distinct working conditions demonstrate a systematic leftward shift of resonance peaks (indicating frequency reduction) as the relative bonding length decreases.  
Figure 7: Comparison between LDV and MEMS sensor. (**a**) Temporal region comparison. (**b**) Frequency-domain signal comparison.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline
**Condition** & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **10** & **11** & **12** & **13** & **  
Quantitatively, in Condition 1 (relative bonding length = 1.0), the NF measures 641 Hz. A reduction to 0.35 in relative bonding length (Condition 14) results in a NF decline to 161 Hz, representing a 74% frequency reduction against a 65% bonding length decrease. This strong correlation confirms that diminished relative bonding lengths induce progressive declines in NF, providing a viable metric for stability assessment.  
Figure 8: Frequency response curve under typical working conditions. (**a**) Frequency response curve of unstable rock mass in working condition 1. (**b**) Frequency response curve of unstable rock mass in working condition 2. (**c**) Frequency response curve of unstable rock mass in working condition 6. (**d**) Frequency response curve of working condition 9. (**e**) Frequency response curve of unstable rock mass under working condition 12. (**f**) Frequency response curve of unstable rock mass under working condition 14.  
The ultimate equilibrium safety factor of unstable rock masses under each working condition was calculated using Equation (2). As shown in Figure 9, the safety factor decreases progressively with reductions in the relative bonding length of structural planes. In the initial state, the theoretical safety factor is 10.98. When the relative bonding length is reduced to 0.4, the safety factor drops to 1.75, and further decreases to 1.34 at a relative bonding length of 0.35. Both the NF and safety factor exhibit broadly consistent declining trends as the relative bonding length diminishes. However, at approximately 0.8 relative bonding length, a divergence emerges between the NF and safety factor trends. This discrepancy may stem from cutting depth inaccuracies during artificial preparation and the inherent heterogeneous anisotropic properties of the rock mass material.
## 4 Evaluation
### Performance Metrics  
We compared DEENT to baseline models regarding accuracy, balanced accuracy, precision, recall, and f1-score.  
**Accuracy**[42] is the fraction of predictions that the model got right (see Equation (1)). Accuracy involves true positives (TP) and true negatives (TN), when the model correctly predicts the non-depressive and depressive tweets, respectively, and false positives (FP) and false negatives (FN), when the model incorrectly predicts the non-depressive tweets and depressive tweets, respectively.  
\[Accuracy=\frac{TP+TN}{TP+TN+FP+FN} \tag{1}\]  
**Balanced accuracy**[43] assesses the performance of classification models on imbalanced datasets, ensuring that both minority and majority classes are important during evaluation. Balanced accuracy is computed by Equation (2) where specificity represents the proportion of actual depressive tweets correctly identified and recall [42] indicates the correct identification of the proportion of actual non-depressive tweets (see Equation (3)). Notably, in the evaluation, we considered recall as a separate metric to analyze each class behavior (see Equation (3)).  
\[Balanced\,Accuracy=\frac{Recall+Specificity}{2} \tag{2}\]  
\[Recall=\frac{TP}{TP+FN} \tag{3}\]  
**F1-score**[42] is commonly used in binary classification problems to analyze and better understand the model performance regarding unbalanced data; this metric is given by Equation (4) which computes the harmonic mean of recall and precision (see Equation (5)).  
**Precision**[44] points out the correct prediction of non-depressive tweets.  
\[F1-score=\frac{2}{\frac{1}{precision}+\frac{1}{recall}} \tag{4}\]  
\[Precision=\frac{TP}{TP+FP} \tag{5}\]  
Figure 4: DEENT-Bert structure.
## 3 Methodology
### Data Pre-Processing
#### 3.2.2 Data Numeralization  
This stage transforms symbolic (non-numeric) data into numerical form by applying _1-N_ numeric coding, where \(N\) corresponds to the number of distinct symbols. In this study, all non-numeric data, including'state', 'proto', and'service' in \(D1\), were converted into numeric values.  
Figure 3: Imbalance ratio of categories in \(D2\).
# Improved PR Control Strategy for an LCL Three-Phase Grid-Connected Inverter Based on Active Damping
## 3 Analysis of the Control Strategy
### Capacitor Current Feedback Active Damping  
Capacitor current feedback active damping will not reduce the low-frequency gain and high-frequency harmonic attenuation capability of the LCL filter and will not increase the additional power loss of the system, thus having very good practical value [14].  
See Appendix A for the capacitor current proportional feedback active damping parameters. Figure 4 is a Bode diagram of loop gain \(T\)(s) after active damping with capacitor current feedback, where \(f_{0}\) is the fundamental frequency, \(f_{\mathrm{c}}\) is the cutoff frequency, and \(f_{\mathrm{r}}\) is the resonant frequency. As can be seen from Figure 4, the introduction of capacitor current feedback can weaken the resonant peak of the LCL filter. With the increase in damping coefficient \(H_{\mathrm{i1}}\), the damping effect of the resonant peak will be better, and at the same time it will not affect the low frequency and high frequency characteristics of the system.
## 1 Introduction  
The advance of technology has enhanced the various areas of knowledge; in the educational process, there have been significant changes in the usual teaching methods and techniques, which has made it possible to develop active and participatory learning. In the virtual training modality, teachers are overloaded with activities related to the educational fact; planning of learning, design of digital didactic material, design of online learning activities, design of online evaluations, monitoring, tutoring, synchronous video classes, feedback, grading and return of assignments, among others. Given this reality, the teacher must select tools that allow him/her to strengthen the teaching practice and provide him/her with inputs to make decisions while developing learning management in the students.  
Information and Communication Technologies (ICT) are in constant innovation and development. An example of their contribution to the learning process is the implementation of e-learning platforms. This contribution has had such an impact that in recent years, these platforms have allowed the generation, organization, and dissemination of knowledge in a simple way and with great availability for anyone with Internet access [1]. Several authors and educational centers maintain that technology plays an essential role in the educational system and that it has a potential for innovation and favorable development prospects [2]; United Nations Educational, Scientific and Cultural Organization(UNESCO) highlights the potential of ICT to disseminate and improve teaching and learning in a wide variety of contexts [3] and [4] adds that ICT also have the power to improve people's quality of life. It also asserts that a future can be seen in which the Internet and other information technologies have the potential to promote impressive improvements in education.  
The quality of education has been a subject of interest for teachers throughout time; the European Higher Education Area (EHEA) considers that \"in order to achieve quality educational systems, new teaching methods for the training of students and innovative methods of assessing learning are necessary\" [5].  
Therefore, along with the rethinking of the educational system and the birth of virtual learning environments, there is also a need for monitoring these interfaces in order to verify compliance with the primary objective: providing a personalized education with quality content regardless of a student's location.
## 5 Conclusions  
1. During the PQ monitoring campaign in the Lithuanian distribution grid, development of a PQ monitor allocation strategy was inhibited by a lack of literature about voltage sags propagation mechanisms: only Lemmas 1 and 2 were known at that time, but their application in practice is challengingly hard (see Section 1). Simulation with the created BRELL-based test grid resulted in the generalization of a voltage sags propagation mechanism by establishing Axioms 3 and 10-14, Lemmas 4, 6, and 7, and Principles 5, 8, and 9 (see Section 4.1). This paper is the first, which not only investigates voltage sags propagation in detail, but also does it in a complex way by considering both voltages, all short-circuit types, interconnection with transient (Principle 5) and unbalance (Principle 9), etc. The method, based on a voltage sag matrix (which has so far been used only for symmetrical faults) has been expanded and adjusted for asymmetrical faults. In addition, the matrix's topological extension along with established definitions in Equations (39) and (40) has been proposed as a further research avenue (see Section 4.3).
2. Regression analysis of the Lithuanian distribution grid showed that almost all correlation coefficients between the number of events and the reliability indexes (SAIFI and SAIDI) are higher than 0.70 (see Section 3.2.1). The values of the tangent of \(\theta\), calculated by Equation (7), are lower than 0.08, which means a low sensitivity (see Equation (8)). This paper is the first which proposes the highlighting of ACR events by a new method for the evaluation of ACR success and ratio (Equations (4) and (5)). All values of ACR success are higher than 0.956, which corresponds with a favorable situation.
3. The 10 kV faults (20% by quantity, 13% by repair duration) had the highest impact on total SAIFI and SAIDI (approximately 80%). The most common events in the 10 kV grid by quantity were ACR, flora, cable line failure, lightning, fauna, and insulator failure (see Section 3.2.2). Since the distributions of all the events listed in Table 16 are not known, probabilistic analysis has been performed by using two models--the Gaussian and Poisson. For estimation of their similarity, when the continuity correction error is calculated using Equations (21) and (22), a new and universal approach is proposed--criteria \(\Delta_{1}\) and \(\Delta_{2}\) are defined by Equations (25) and (26) (see Section 2.4).  
Conceptualization, V.L. and V.R.; methodology, V.L., V.R. and G.K.; software, V.L., V.R., G.K. and D.N.; validation, V.L., V.R., G.K. and D.N.; formal analysis, V.L., V.R., G.K. and D.N.; investigation, V.L. and V.R.; resources, V.L. and V.R.; data curation, V.L.; writing--original draft preparation, V.L. and V.R.; writing--review and editing, V.L., V.R. and G.K.; visualization, V.L.; supervision, V.R. and G.K.; project administration, V.L. and V.R. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Lithuanian Energy Institute.
## 2 Methodology
### Feature Extraction  
Complete distorted sinusoidal signal data points over 5 cycles are split into multiple sets based on the sliding window approach. Each set consists of 15 data points since the window size is considered as 15. Four features called slope (m), intercept (c), correlation coefficient (R) and root mean square error (RMSE) are extracted from each set that consists of 15 data samples by comparing unity amplitude fundamental signal using below equations  
\[m=\frac{n*\sum_{i=1}^{n}v_{k}t_{k}-\sum_{i=1}^{n}t_{k}\sum_{i=1}^{n}v_{k}}{n* \sum_{i=1}^{n}(t_{k})^{2}-(\sum_{i=1}^{n}(t_{k})^{2})} \tag{2}\]  
\[c=\frac{\sum_{i=1}^{n}v_{k}(\sum_{i=1}^{n}(t_{k})^{2})-(\sum_{i=1}^{n}t_{k})*( \sum_{i=1}^{n}t_{k}v_{k})}{n*\sum_{i=1}^{n}(t_{k})^{2}-(\sum_{i=1}^{n}(t_{k})^{ 2})} \tag{3}\]  
Figure 2: Noise signals used for dataset preparation. (**a**) Noise level 5%. (**b**) Noise level 15%. (**c**) Noise level 20%. (**d**) Noise level 30%. (**e**) Noise level 40%. (**f**) Noise level 50%.  
\[R=\frac{n*\sum_{i=1}^{n}v_{k}u_{k}-\sum_{i=1}^{n}u_{k}\sum_{i=1}^{n}v_{k}}{(\sqrt{n* \sum_{i=1}^{n}(u_{k})^{2}-(\sum_{i=1}^{n}(u_{k})^{2}))}-(\sqrt{n*\sum_{i=1}^{n}( v_{k})^{2}-(\sum_{i=1}^{n}(v_{k})^{2})})} \tag{4}\]  
\[RootMeanSquareError(RMSE)=\sqrt{\frac{\sum_{i=1}^{n}(u_{k}-v_{k})^{2}}{n}} \tag{5}\]  
where m is slope, c is intercept, R is correlation coefficient, n is number of samples, \(t_{k}\) is time value of \(k^{th}\) data point, \(v_{k}\) is voltage magnitude of distorted signal at time \(t_{k}\) and \(U_{k}\) is voltage magnitude of unity fundamental signal at time \(t_{k}\).  
These four features are the input features for the selected window. If ZCP exists within the window then the class label (output variable) is 1, i.e., ZCP class. If ZCP does not exist within the window then the class label is 0, i.e., NZCP class. Hence, every sample in the dataset consists of four input features and one output variable that represents the class.  
Figure 3: Sinusoidal signals with various THD levels used for dataset preparation. (**a**) THD level, 10%. (**b**) THD level 20%. (**c**) THD level 30%. (**d**) THD level 40%. (**e**) THD level 50%.