esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results and Discussion  
The validation of our simultaneous feature selection, discretization, and parameter tuning for LM-WLCSS classifiers is carried out in this section. The results on performance recognition and dimensionality reduction effectiveness are presented and discussed. The computational experiments were performed on an Intel Core i7-4770k processor (3.5 GHz, 8 MB cache), 32 GB of RAM, Windows 10. The algorithms were implemented in C++. The Euclidean and LCSS distance computations were sped up using Streaming SIMD Extensions and Advanced Vector Extensions. Subsequently, the Ameva or ur-CAIM criterion used as an objective function \(f_{3}\) (15) is referred to as MOFSD-GR\({}_{Ameva}\) and MOFSD-GR\({}_{\text{ur-CAIM}}\) respectively. On all four subjects of the Opportunity dataset, Table 2 shows a comparison between the best-provided results by [PERSON] et al. [19], using their proposed classifier fusion framework with a sensor unit, and the obtained classification performance of MOFSD-GR\({}_{Ameva}\) and MOFSD-GR\({}_{\text{ur-CAIM}}\). Our methods consistently achieve better \(F_{w}\) and \(F_{\text{w}_{\text{  
Figures 3 and 4 depict the number of discretization points yielded by the two discretization strategies across all 17 gestures of the Opportunity dataset. From the results, the following assessment can be made. 1. As intended by the nature of Ameva, MOFSD-GR\({}_{Ameva}\) yields a small number of cut points close to the constraint imposing that the template be made of at least three distinct discretization points (18). However, this advantage seems to limit the exploration capacity of C-MOEA/DD since only half of the original features are discarded. 2. In contrast, MOFSD-GR\({}_{\text{tur-CAIM}}\) tends to generate larger discretization schemes than MOFSD-GR\({}_{Ameva}\). Since the ur-CAIM criterion aggregates two conflicting objectives (CAIM aimed to generate a lower number of cut points, and the pair CAIR and CAIU advocates a larger number), compromises are made. Tables 3 and 4 present more detailed results. They recapitulate the average, \(\mu\), and standard deviation, SD, of the number of cut points (\(\#dp\)) produced and features selected (\(\#d\)) by MOFSD-GR\({}_{Ameva}\) and MOFSD-GR\({}_{\text{tur-CAIM}}\), respectively. Please note that no substantive conclusions could be drawn from the intersections between the following sets of selected features from (1) a particular subject, (2) a particular gesture, and (3) a particular gesture and fold due to the one-vs.-all decomposition approach used for this multi-class problem. Figure 4: Box plot representation for discretization (number of cut points). Figure 3: Box plot representation for feature selection (reduction rate in %). \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Subject 1**} & \multicolumn{4}{c}{**Subject 2**} & \multicolumn{4}{c}{**Subject 3**} & \multicolumn{4}{c}{**Subject 4**} \\ \hline
**Gesture Names** & \(\mathbf{{}_{H\!A}
## 4 Material and Methods
### Transcriptome Sequencing  
#### 4.3.1 RNA Extraction and Library Preparation  
Total RNA was extracted from 36 samples (4 pepper varieties \(\times\) 3 developmental stages \(\times\) 3 biological replicates) using FastPure(r) Plant Total RNA Isolation Kit (Vazyme, China). The purity and quantitative analysis of extracted RNA were evaluated using the NanoDrop 2000 ultraviolet spectrophotometer (Science Corporation, USA). The integrity of RNA was evaluated using the Agilent 2100 Bioanalyzer (Agilent Technologies, Santa Clara, CA, USA). The qualified total RNA was used to construct the cDNA library.
# Predicting Saudi Stock Market Index by Using Multivariate Time Series Based on Deep Learning
###### Abstract  
Time-series (TS) predictions use historical data to forecast future values. Various industries, including stock market trading, power load forecasting, medical monitoring, and intrusion detection, frequently rely on this method. The prediction of stock-market prices is significantly influenced by multiple variables, such as the performance of other markets and the economic situation of a country. This study focuses on predicting the indices of the stock market of the Kingdom of Saudi Arabia (KSA) using various variables, including opening, lowest, highest, and closing prices. Successfully achieving investment goals depends on selecting the right stocks to buy, sell, or hold. The output of this project is the projected closing prices over the next seven days, which aids investors in making informed decisions. Exponential smoothing (ES) was employed in this study to eliminate noise from the input data. This study utilized exponential smoothing (ES) to eliminate noise from data obtained from the Saudi Stock Exchange, also known as Tadawul. Subsequently, a sliding-window method with five steps was applied to transform the task of time series forecasting into a supervised learning problem. Finally, a multivariate long short-term memory (LSTM) deep-learning (DL) algorithm was employed to predict stock market prices. The proposed multivariate LSTMDL model achieved prediction rates of 97.49% and 92.19% for the univariate model, demonstrating its effectiveness in stock market price forecasting. These results also highlight the accuracy of DL and the utilization of multiple information sources in stock-market prediction.  
deep learning; predictions; time series; LSTM; multivariate; univariate +
Footnote †: journal: Applied sciences
# Computational Simulation of 3D Fatigue Crack Growth under Mixed-Mode Loading
## 4 Conclusions  
In this study, mixed-mode (I/II) fatigue crack growth investigations were performed on a single edge cracked plate with three holes and different thicknesses as well as for a CTS specimen for different thicknesses and loading angles. It was observed that the geometry thickness did not affect the crack growth trajectory. In contrast, when the thickness of the specimen increased or decreased, it had a more significant impact on the stress and strain distribution. As a result, the stress-strain field at the crack tip tended to move from the plane stress state to the plane strain state as thickness increases, asserting that the crack tip is under tension in all three dimensions and the plastic zone will be constrained in a small scope. Accordingly, as the geometry thickness is increased, the equivalent Von-Mises stress and the maximum principal stress were decreased with approximately the same ratio of increasing the thickness. The fatigue crack grew toward the hole, as predicted, due to unbalanced stresses at the crack tip induced by the hole. In these simulation sequences, holes act as a crack stopper and attract a crack trajectory to grow. These results confirm the algorithm's ability to identify crack-stopping holes in damage tolerance designs. The predicted fatigue crack propagation paths, stress intensity factors, and fatigue life cycles were validated by other researchers' experimental and numerical results.  
This research received no external funding.  
**Institutional Review Board Statement**: Not applicable.  
**Informed Consent Statement**: Not applicable.  
**Data Availability Statement**: Not applicable.  
**Conflicts of Interest**: The author declare no conflict of interest.
## 3 Results
### Spatial and Temporal Characteristics of WUE
#### 3.1.2 Variation Trend of WUE  
The WUE in IM showed an increasing trend from 2001 to 2018 (Figure 4). The mean WUE of all the vegetation types in the study area increased significantly (\(p<0.05\)), with an increasing rate of 0.0071 g C m\({}^{-2}\) mm\({}^{-1}\) yr\({}^{-1}\). The minimum in WUE occurred in 2007 (1.24 g C m\({}^{-2}\) mm\({}^{-1}\)), while the maximum occurred in 2012 (1.52 g C m\({}^{-2}\) mm\({}^{-1}\)) (Figure 4a). Similarly, a rising trend was observed in the mean WUE of different vegetation types. Figure 4b shows the dependence of the WUE trend on the vegetation types in the following descending order: forest (0.0091 g C m\({}^{-2}\) mm\({}^{-1}\) yr\({}^{-1}\)), swamp (0.0084 g C m\({}^{-2}\) mm\({}^{-1}\) yr\({}^{-1}\)), meadow (0.0068 g C m\({}^{-2}\) mm\({}^{-1}\) yr\({}^{-1}\)), shrub (0.0064 g C m\({}^{-2}\) mm\({}^{-1}\) yr\({}^{-1}\)), and cultivated vegetation (0.0057 g C m\({}^{-2}\) mm\({}^{-1}\) yr\({}^{-1}\)).  
Figure 4: Variation of (**a**) annual mean WUE and (**b**) the dependence of WUE trend on different vegetation types in Inner Mongolia from 2001 to 2018.  
Figure 3: Spatial distribution of (**a**) annual mean WUE and (**b**) its dependence on different vegetation types in Inner Mongolia over 2001–2018.  
Spatially, the time rate of the mean WUE varied between \(-\)0.14-0.17 g C m\({}^{-2}\) mm\({}^{-1}\) yr\({}^{-1}\) (Figure 5a). Areas with increasing WUE accounted for about 74.14% of the study area, mainly in the southeast and northeast regions. Areas with a decreasing WUE accounted for about 25.86%, mainly in Baotou, southwest Xilingol League, middle Chifeng, west Hinggan League and east Hulunbuir. According to the WUE trend and F-test results, the variation trend of the WUE was divided into four classes according to the significance level of \(p<0.05\): significant increase, insignificant increase, significant decrease and insignificant decrease (Figure 5b). The results showed that 28.08% of the total area passed through the significance test. Specifically, areas with significant increases in WUE accounted for 24.47% of the study area, mainly in Erdos and Hohhot in the southwest, southwest Chifeng, north Tongliao, east Hinggan League, middle and north Hulunbuir, and the vegetation types were mainly meadow, forest and cultivated vegetation. Areas with significant decreases in WUE accounted for 3.61%, mainly in meadows in the middle Chifeng and forests in northeast Hulunbuir. Areas with insignificant increases and insignificant decreases in WUE accounted for 49.67% and 22.25%, respectively.
## 4 Design of the DRFTM
### Trust Evaluation Based on Random Forest
#### 4.2.3 Trust Status Determination of Nodes  
This study utilizes the random forest algorithm to categorize trust levels for trust evaluation. The determination of a node's trust status directly corresponds to the classification outcome produced by the random forest algorithm. The trust level of nodes is represented by \(\{C_{1},C_{2},C_{3}\}\), which corresponds to [high trust, ordinary trust, low trust], respectively. Every decision tree is a classifier. For the trust evidence set obtained in Section 4.1, it is assumed that it contains \(K\) decision trees \(\{H_{1},H_{2},\ldots,H_{K}\}\), and \(K\) trees will have \(K\) kinds of trust judgment results. The random forest algorithm combines all trust evaluation results and determines the final trust status of a node by majority voting, selecting the result with the highest number of votes. The implementation steps of the random forest algorithm are presented as follows:  
Input: given training sample data and test sample data.  
1. The original training set contains \(N\) samples. Each time a tree is constructed, \(n\) samples are randomly chosen from it to form the training set for that tree. This process is repeated \(K\) times, resulting in the creation of \(K\) training sample sets. Finally, these sample sets are used to build \(K\) classification trees.
2. The feature dimension of the samples is \(M\), and we need to choose a constant \(m<M\), and \(m\) features are randomly selected from the \(M\) features.
3. Each tree is prepruned according to pruning strategies during growth.
4. Multiple classification trees are assembled into a random forest. This random forest classifier is then used to evaluate and classify new data. The final classification result is determined by the voting consensus of the individual decision tree classifiers.  
Voting strategy: The decision tree \(H_{i}\) will predict a final result from the set of category labels \(\{C_{1},C_{2},C_{3}\}\). The predicted output of \(H_{i}\) on the training set \(x\) is expressed as a three-dimensional vector \(\left(H_{i}^{1}(x),H_{i}^{2}(x),H_{i}^{3}(x)\right)\), where \(H_{i}^{j}(x)\) is the output of \(H_{i}\) on the category marker \(C_{j}\). The absolute majority voting method is used to determine the final classification result, meaning that if a label receives more than half of the votes, it is predicted as that label; otherwise, it is labeled as ordinary trust:  
\[H(x)=\left\{\begin{aligned} C_{j},&\text{if }\sum\limits_{i=1}^{K}H_{i}^{j}(x)>0.5\sum \limits_{n=1}^{N}\sum\limits_{i=1}^{K}H_{i}^{n}(x)\\ \text{Ordinary trust},&\text{otherwise}\end{aligned} \right.\text{.} \tag{20}\]
## 3 Object-of-Interest Annotation  
This section presents an easy and user-friendly approach to annotate those scenes in a large video collection that contain a specified object-of-interest. These annotations are well suited to improve text-based video retrieval, especially when a user searches for specific objects or scenes. The left part of Figure 2 shows the corresponding annotation workflow. The entire video collection is first pre-processed to extract keyframes and visual features. Next, a few initial object instances are interactively collected starting from a single keyframe that shows this object. In this process, the region of each object instance is automatically selected. After this, appropriate recognition approaches are selected that fit to the visual attributes of the object in an integrated analysis-evaluation approach. Eventually, the object is automatically annotated in all videos of the online portal.  
The right part of Figure 2 shows the database schema that is used to store the results of this object annotation. _Objects-of-interest_ are given with one or more user-generated free text labels to enable retrieval tasks. These labels can be ambiguous as the same object might be annotated differently [28], but it is out of the scope of this work to dissolve such ambiguities. Furthermore, metadata (like a user identification, annotation date, and an interaction protocol) are stored together with the selected _recognition approaches_ that are used for the automatic annotation of this object. _Object instances_ capture the object-of-interest that is shown in a keyframe with an optional region (arbitrary shaped polygon) and a user feedback flag (correct or false annotation). _Videos_ and _keyframes_ are derived from the _media object_ class and they contain a media reference and metadata. The keyframes are either stored in small resolution or extracted on the fly from the video at the specified time point. In the latter case, the media reference is left empty. On the fly generation is especially reasonable when this annotation system is integrated to a video portal that contains suitable video summarization and browsing capabilities. The schema can be extended by further media object entities, such as shots and scenes, when necessary. Shot entities are, for example, used in the TRECVID 2011 experiments in this work. Furthermore, the proposed annotation schema contains _visual features_ that are serializable to binary form which makes them suitable for storage in all kinds of databases and file systems. Further details about the storage of recognition approaches and visual features are given in Section 4.
## 4 Numerical Simulation Analysis
### Seismic Response Influence of Structure-Electrical Equipment Interaction
#### 4.3.1 Acceleration Responses  
The acceleration amplification factors under 0.40 g, with the interaction between electrical equipment and structure, are shown in Table 10, in which the acceleration amplification factor is defined as the ratio between the peak acceleration of both the layer and input seismic motion. Similarly, the soil acceleration amplification factor is defined as the value at the same level of the structure.  
(1) Under the same direction and earthquake motion, acceleration amplification factors of soil and structure gradually increase; acceleration amplification factors in the horizontal X-direction are greater than those in the horizontal Y-direction. Meanwhile, the peak acceleration amplification factors of structure and soil are 1.070 and 1.772 (in the horizontal X-direction under Taft motion) respectively, which means that seismic responses are more severe in these cases than in other cases.  
(2) Under different earthquake motions, acceleration amplification factors at the top of electrical equipment are different from each other. The peak acceleration amplification factor on the second floor under Taft motion is 3.52 (in the horizontal or the X-direction) which is much greater than 2.0 given in Code for the seismic design of electrical installations (2013, China). The acceleration amplification factors of electrical equipment in the X-direction are obviously greater than those on the same floor, which suggests that the acceleration response of the structure and electrical equipment is not synchronous. Therefore, the interaction between structure and equipment should be taken into account in the seismic design of an underground substation.
## 4 Results and Discussion
### Effects of Lignin on Soil Mix S12
#### 4.1.3 Unconfined Compressive Strength and Indirect Tensile Strength Tests  
Unconfined compressive strength and indirect tensile strength tests allowed obtaining information on the variation of the compressive and tensile strength as a function of lignin content.  
As can be seen in Table 4, the indirect tensile strength obtained for the lignin-treated soil was slightly higher than the ITS obtained for the untreated clay. The studies analysed in Section 2.1 do not include an ITS test.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & \multicolumn{3}{c}{**Tests**} \\ \cline{2-3}
**Lignin Content (\%)** & \multicolumn{3}{c}{**Curing Time: 7 Days of Air Drying**} \\ \cline{2-3}  & **ITS (daN/cm2)** & **UCS (N/mm2)** \\ \hline
0\% (untreated soil) & \(1.1\) & \(1.4\) \\
0.40\% & \(1.2\) & \(1.6\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Effect of lignin content on ITS and UCS values.  
Figure 7: Time–swelling curve for the untreated soil and the 0.40% lignin-treated soil.  
Table 4 shows that lignin provided a slight increase in the UCS of the treated soil when compared with the natural soil.  
The increase in strength for lignin-treated soil compared to natural soil was reported in several previous studies [56; 57; 65]. The strength of lignin-stabilised soil may be higher than that of untreated soil in wet conditions [63]. Overall, the mechanical properties of lignin-treated soil increase with lignin content and the curing period [56; 57]. In some cases, the excessive lignin content causes a decrease in the strength of lignin-treated soil [56; 57]. Strength loss is attributable to the formation of weak spots due to the excessive content of the additive [56].  
However, [PERSON] et al. [65] concluded that lignin-treated clay soils allow recording the highest increase in both the dry and wet UCS compared to other soils stabilised with non-traditional additives.
## 6 Strategic Planning and Risk Mitigation  
### Protective Eyewear  
Adaptive visors are critical for mitigating two primary risks during space missions: high-energy radiation exposure and mechanical trauma from micro-debris. These visors incorporate advanced radiation-blocking coatings to shield ocular tissues from ultraviolet (UV) rays and galactic cosmic rays (GCRs), which accelerate conditions like keratitis and corneal scarring [76]. Nanomaterial coatings provide effective GCR protection while maintaining lightweight functionality for EVAs [25]. Impact-resistant materials, such as polycarbonate or advanced polymers, are integrated into visor designs to prevent corneal abrasions or punctures caused by micro-debris impacts [76].  
Emerging technologies, like electrochromic visors, offer real-time adaptability to varying light conditions. By incorporating liquid crystal or photochromic materials, these visors automatically modulate opacity based on ambient light intensity, reducing glare and eye strain during transitions between light and shadow [32]. Active cooling systems further enhance visibility by preventing fogging in enclosed helmets, ensuring optimal performance during critical tasks.  
Visors for deep-space missions also incorporate layered composite materials to disperse the energy of high-velocity micro-meteoroid impacts, enhancing durability. Self-healing materials, currently under investigation, could autonomously repair small abrasions, extending the lifespan and reliability of protective visors [25].  
Radiation shielding continues to advance, with innovations focusing on hydrogen-rich materials and magnetic shielding systems. These materials provide superior attenuation of high-energy particles while maintaining visor structural integrity. For long-duration missions, visors integrated with dosimeters offer real-time radiation exposure monitoring, enabling astronauts to respond promptly to elevated radiation levels [26; 51].  
Future developments aim to reduce visor weight further while enhancing durability and functionality. Customizable designs tailored to individual astronauts and mission-specific hazards promise a new generation of protective eyewear, ensuring both safety and performance during deep-space exploration.
# Mapping Surface Flow Velocity of Glaciers at Regional Scale Using a Multiple Sensors Approach
###### Abstract  
We explore and compare the capabilities and limitations of different optical sensors (Sentinel-2/ESA, Landsat 7/8/USGS, Venus/CNES-ISA, Pleiades/AirbusD&S and Planet Labs images) for mapping the surface speeds of mountain glaciers on a regional scale. We present here our automated workflow designed to download data from institutional or commercial servers, prepare images, launch the feature tracking algorithm, calibrate glacier surface speeds, and our post-processing treatment to obtain filtered and time-averaged velocity maps. We applied our methodology to three regions: (1) the European alps; (2) the Peruvian Cordillera Blanca; and (3) the Southern Alps of New Zealand for years 2017 and 2018 and quantified ice velocity for every possible repeat cycle from few days up to 400 days. For these regions, we demonstrate the ability of our processing chain to derive precise time-averaged ice flow maps. The statistical analysis of the results provided by each individual repeat cycles shows that velocity mapping from Sentinel-2 is about twice more precise than that from Landsat 7/8. If Sentinel-2 captures more details than Landsat, some of the smallest glaciers (<250 m wide) remain challenging. Given the estimated precision for Sentinel-2, we also conclude that velocity fluctuations of the order of 10 m/yr can only be captured with repeat cycles longer than 60 days. Comparing Sentinel-2 with Pleiades, Planet and Venus imagery, we finally highlight the advantages of high-resolution sensors to map glacier surface speed with finer details in space and time.  
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