esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Theoretical Foundation
### Probabilistic Roadmap  
The probabilistic roadmap (PRM) algorithm provides a collision avoidance path planning successfully used for mobile robot applications in the presence of obstacles [34]. This method is extremely efficient due to the ability to design paths quickly and the prevalence of the shortest path. In this method, a random sample of the configuration space is initially generated using a uniform probability distribution. In sequence, the algorithm tests the samples for collision. The trajectory is generated from \(q_{start}\) to \(q_{goal}\) using the points where a collision is not detected [35; 36].  
The logic of the PRM is based on previously analyzing the robot's trajectory from a predetermined map. Therefore, with this knowledge of the location where the robot will transit, it is possible for it to calculate the route that it will follow. Therefore, the SCARA manipulator will calculate the route effectively, aiming to distance all obstacles along the path from the robot [35].
## 3 Results and Discussion  
### Extraction and Fraction Yield  
The ethanol extract of _S. japonica_ (2.5 kg wet weight) yielded 88.37 g of crude extract (3.58% yield), among which 50 g was used for fractionation. The aqueous fraction showed the highest yield (73.78%), followed by the hexane fraction (14.39%), butanol fraction (8.62%), chloroform fraction (1.23%), and ethyl acetate fraction (0.32%) (Table 1). The high solid yield from the aqueous fraction can be explained by the large quantity of sugar, protein, glycosides, organic acid, tannins, salts, and mucus contained in kelp [18]. Phenolic compounds and carotenoids have a high solubility in non-polar solvents due to their low polarity. Although the butanol and aqueous fractions showed overall high yields, the total phenolic contents and fucoxanthin contents were not detectable in these two fractions. Therefore, these two fractions were excluded from further analysis.
## 1 Introduction
They report that the microstructure of AZ61 filler welds show a reduction in grain size of HAZ, compared with welded joints made without filler [21]. [PERSON] et al. [22] investigate the influence of welding speed on the microstructure and mechanical properties of AZ31 GTA welds prepared with a pulsing technique. They found that the welded joint prepared using a high welding speed of 135 mm/min exhibit superior tensile properties (YS: 189 MPa, UTS: 193 MPa, % El: 4.5) compared with low welding speed (YS: 143 MPa, UTS: 183 MPa, % El: 5.5). They observe that the superior mechanical properties of welds prepared with a welding speed of 135 mm/min are because of the fine grains and finely dispersed precipitates in the weld [22]. [PERSON] et al. [23] report that post-veld hot rolling of AZ31 GTA welds show improved mechanical properties (YS: 240 MPa, UTS: 297 MPa, % El: 3.5) compared to no post-veld rolling (YS: 108 MPa, UTS: 172 MPa, % El: 1.4). The superior mechanical properties after post-veld hot rolling are attributed to the strain hardening and twin boundaries. However, the ductility of the post-veld rolled weldments is lower than the base metal (YS: 167 MPa, UTS: 251 MPa, % El: 19). The mechanical properties of the welds are adversely impaired by columnar and coarse dendritic microstructures in the fusion zones. For better mechanical properties of the weldments, columnar grains should be replaced with fine equiaxed grains. It is well known that the ratio of G/R, where R is the growth rate, and G is the thermal gradient, determines the morphology of solidification in any given alloy [23]. Various techniques (ultrasonic vibration, double-sided arc welding, magnetic oscillation, exciting the electric arc) were employed to promote columnar to equiaxed transition, and enhance the % of equiaxed grains in the weld [24; 25; 26; 27]. However, changing the welding parameters, and the addition of inoculants, are the most common and straightforward strategies employed in industrial welding practice. Welding speed is a crucial parameter in GTA welding because it determines the full penetration with no excessive heat build-up. Increasing welding speed also allows for more constitutional supercooling at the solidification front, which aids in heterogeneous nucleation and grain refinement in the welds [28; 29]. There are few studies on the impact of weld heat input on the grain refinement of Mg welds. The present study evaluated the effect of weld heat input on the microstructure, hardness, and tensile properties of AZ31 GTA weldments. Further, the effect of the welding speed, and consequent variation in weld heat input, was correlated to the thermal history and the grain refinement of the fusion zone.
# A Review on Ultrafast Laser Enabled Excellent Superhydrophobic Anti-Icing Performances
## 1 Introduction
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multirow{2}{*}{**Accuracy**} & \multicolumn{2}{c}{**Structural**} & \multirow{2}{*}{**Applicable Materials**} & \multicolumn{2}{c}{**Large-Scale**} & \multirow{2}{*}{**Refs.**} \\  & & **Tunability** & & & **Fabricating** & \\ \hline Ultrafast laser processing & nm & \(\surd\) & Any materials & \(\surd\) & [62, 63, 64] \\ Chemical treatment & nm & \(\times\) & Metals & \(\surd\) & [65, 66, 67] \\ Photolithography & nm & \(\surd\) & Silicon & \(\times\) & [68, 69] \\ Template methods & nm & \(\surd\) & Polymer, metal & \(\surd\) & [70, 71, 72] \\ Machining & \(\upmu\)m & \(\surd\) & Any materials & \(\surd\) & [73, 74] \\
3D printing * & \(\upmu\)m & \(\surd\) & Polymer, metal, ceramics & \(\times\) & [35, 75] \\ Sol–gel methods & nm & \(\times\) & Polymer & \(\surd\) & [76, 77] \\ \hline \hline \end{tabular}  
* Here, we take two-photon lithography (TPL) as the representative of 3D printing.  
\end{table} 
Table 1: Comparison of common micro-nanofabrication techniques in terms of fabrication rate, accuracy, structural tunability, applicable materials, and large-scale fabricating. The next section introduces the recent progress of ULSASs in the three aspects of passive anti-icing, passive/active combined anti-icing, and their practical outdoor applications in detail. Combining the storylines from fabrication to mechanisms and then to applications, we present current challenges in moving ULSASs toward practical applications. Finally, we give a conclusion and provide prospects for the future development of ULSASs. Figure 2: Ultrafast laser-fabricated superhydrophobic anti-icing surfaces: fabrication, mechanism understanding and surface development. Reproduced with permission from [78], copyright (2022) Springer; reproduced with permission from [79], copyright (2022) Nature Publishing Group; reproduced with permission from [80], copyright (2020) American Chemical Society; reproduced with permission from [81], copyright (2023) American Chemical Society; reproduced with permission from [6], copyright (2023) American Chemical Society; reproduced with permission from [82], copyright (2022) Elsevier.
## 3 Case Study
### Data Preprocessing  
To ameliorate the prediction effect of the algorithms, the EPB data needs to be preprocessed before establishing the model. Hence, we proposed a data standard preprocessing method for EPB tunneling parameters, which included three phases: data segmentation, noise reduction, and data standardization. The advance rate (\(v\)) is used as an example to illustrate the preprocessing process, as shown in Figure 7.
# A Novel Inductor-Based Non-Dissipative Equalizer
## 3 Simulation
### Equalization Simulation for SSIE, PAE and NIBNDE with Six Cells  
As shown in Figure 6, SSIE, PAE and NIBNDE are applied to six cells in order to compare the balancing speed between them. The number of different distribution sequences of the six batteries is \(A_{6}^{6}=720\), of which three hundred and sixty samples are independent. In this paper, we chose fifty random samples to simulate and calculate the average balancing time. The initial voltages of the six batteries are shown in Table 4, and the equalization threshold is 3 mV. Figure 6 shows the circuit diagrams of SSIE, PAE and NIBNDE with six cells.  
Figure 7 presents the simulation waveforms in SSIE, PAE and NIBNDE with the distribution sequences of ABCDEF. VB\(x\) (\(x=1\)-\(6\)) represents the voltage of \(B_{x}\). Table 5 gives a comparison of the data. Compared to the SSIE, the proposed NIBNDE requires four less MOSFETs and two more inductors, while reducing the average balancing time by 34.52%. Compared to the PAE, the NIBNDE uses the same MOSFETs and two less inductors. The balancing time of the PAE is 75.19% less than the NIBNDE.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **A** & **B** & **C** & **D** & **E** & **F** \\ \hline Voltage & 3.98 & 3.96 & 3.94 & 3.92 & 3.90 & 3.88 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The initial cell voltages of the six cells (voltage unit: V).  
Figure 6: Circuit diagrams of SSIE (**a**), PAE (**b**) and NIBNDE (**c**) with six cells.  
In summary, although the SSIE uses only one inductor, the equalization time and the number of MOSFETs for the NIBNDE are both less than the SSIE. The equalization speed of the PAE is faster than the NIBNDE, but they use the same number of MOSFETs and the NIBNDE uses two less inductors than the PAE.
# Analysis of Numerical Artifacts Using Tetrahedral Meshes in Low Frequency Numerical Dosimetry
## 4 Dosimetric Models
### Human Head  
_Anatomical 3D Model_  
Voxel and tetrahedral discretizations are compared considering a more challenging model, the realistic human adult head _Colin27 Avenue Brain_ (also known as Average Colin). Average Colin is an adult brain atlas [30] that consists of four tissues: skull, cerebrospinal fluid (CSF), gray matter (GM) and white matter (WM). This atlas was obtained by scanning the brain of [PERSON] 27 times (hence the name Colin27) over the course of a few months. The images were combined to create an average brain with high structure definition. The tetrahedral mesh was created by [PERSON] (more information can be found in [31]) and it is freely available for download [32]. The model is shown in Figure 4a.  
Figure 4: _Cont._  
Figure 3: Multilayered sphere. The various layers are highlighted by different colors. In the inset the stair-casing effect due to the voxel-discretization is displayed. The source field is created by a coil with a 5 cm radius and located 13 cm above the center of sphere.  
The following tissue conductivities are used: skull 0.02 S/m, cerebrospinal fluid 2 S/m, grey matter 0.02 S/m and white matter 0.02 S/m. The operating frequency is 50 Hz.
# Spatiotemporal Variations in Vegetation Canopy Interception in China Based on a Revised Gash Model
###### Abstract  
Vegetation canopy interception (lc) of precipitation is a considerable component of the global hydrological cycles. Although the measurement and modeling of canopy interception have been explored worldwide at the individual, stand or ecosystem scale, it is still unclear how to recognize this process at the regional or global scales within the context of global climate change. In this study, a revised Gash model was employed to estimate canopy interception based on remote sensing and meteorological data. The spatial and temporal variations in lc were investigated and the main environmental factors were explored in China for the 2000-2018 period. The results showed that the revised Gash model performed well in modeling canopy interception at the regional scale compared with the PML_V2 dataset product and the in-situ measurements. The average annual Ic in China from 2000 to 2018 was 166.55 mm, with a significant decreasing spatial pattern from the Southeastern to the Northwestern regions. The ratio of canopy interception to precipitation (lr) displayed a similar spatial pattern, with an average value of 22.30%. At the temporal scale, the mean annual Ic significantly increased at a rate of 1.79 mm yr\({}^{-1}\) (\(p<0.01\)) during the study period, and the increasing trend was more pronounced during the 2000-2009 period, at a rate of 3.34 mm yr\({}^{-1}\) (\(p<0.01\)). In most vegetation types, except for the deciduous broad-leaved forest and temperate desert, canopy interception showed a significant increasing trend (\(p<0.01\)). Precipitation, temperature, and the normalized differential vegetation index (NDVI) were considered to be the main factors affecting the variations of Ic in China during the last two decades, with specific dominant factors varying in different areas. Specifically, precipitation was considered to control the variations of Ic in the Northwestern regions, temperature mainly influenced the Southern regions, and the NDVI was identified as the main factor in regions where significant ecological conservation projects are established, such as the Loess Plateau. Our findings are expected to not only contribute to the understanding of regional ecohydrological cycle but also provide valuable insights into the methodology of interception modeling at the regional and global scales.  
canopy interception; revised Gash model; remote sensing; vegetation types; China 2022
## 4 Experimental Results  
### Experimental Setup  
We evaluated the forecasting performance of various techniques and models using the mean absolute percentage error (MAPE) as a metric for calculating the error. MAPE is a measure indicating the extent to which the predicted value differs from the observed value. The formula is as follows:  
\[\text{MAPE}=\ \frac{1}{n}\sum_{i=1}^{n}\left|\frac{A_{i}-\ F_{i}}{A_{i}}\right| \tag{10}\]  
where \(A_{i}\) is the actual value, and \(F_{i}\) is the forecasted value.  
In this section, we present the experimental results. To demonstrate the advantages of our proposed method that utilized SOM and the SELM, we compared forecasting performances of three different models: Model 1, Model 2, and Model 3. **Model 1** is a baseline model that uses a single prediction technique to produce an hourly load profile of the next day without using any other techniques such as clustering or ensemble. We used MLR, an ANN, SVR, RF, ADB, XGB, and a GBM for the single prediction technique. **Model 2** uses the same techniques as Model 1 for the forecasting tasks; however, before the forecasting task, the dataset is clustered into several clusters using SOM. The same number of forecasting models comprising homogeneous prediction techniques is then trained on the clusters. When it makes a prediction, a data point in the test dataset is assigned to one  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Column** & **1-24** & **25–48** & **49–51** & **52–54** \\ \hline Data & L(\(d-1\)) & L(\(d-7\)) & \(T_{\text{avg}}\), \(T_{\text{max}}\), \(T_{\text{min}}\) & \(T_{f,\text{ avg}}\), \(T_{f,\text{ max}}\), \(T_{f,\text{ min}}\) \\ \hline \end{tabular}
\end{table}
Table 1: Input data matrix.  
of the clusters and the corresponding forecasting model predicts an hourly load profile of the next day. **Model 3** also includes the clustering process, but it applies the SELM in the forecasting task instead of single prediction techniques. Model 3 is our proposed method. The models are represented in the Figure 5.
## 4 Results
SEM analysis of the post-test condyle and glenoid fossa specimens revealed a clear demarcation between the areas of the surface which underwent wear and those that were not physically affected (Figures 13 and 14). While this demarcation was marked among acrylic condyle and fossa specimens, irrespective of the opposing material, it was observed to a lesser extent in titanium specimens and almost to a negligible extent (at 500\(\times\) magnification) among zirconia specimens. Even within titanium specimens, the demarcation of wear facets was less pronounced among condyle and fossae which were tested against acrylic fossae and condyles, respectively. Figure 11: Bar graph showing the mean difference between the pre-test and post-test weights of the different prosthetic TMJ parts after biomechanical simulation testing. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Sample Material (Coupled)**} & \multicolumn{2}{c}{**Opposing Material**} & \multicolumn{2}{c}{**Pre-Test Weight (in Grams)**} & \multicolumn{2}{c}{**Post-Test Weight (in Grams)**} & \multicolumn{2}{c}{**Paired T-Test Values**} & \multicolumn{2}{c}{**95\% C.L.**} \\ \cline{2-13}  & **Material** & & **Mean** & _S.D._ & _Mean_ & _S.D._ & _Mean Diff._ & _S.E._ & _p-Value_ & _T-Value_ & 95\% C.L._ \\ \cline{2-13}  & **(Clenoid Foss)** & **(Clenoid Foss)** & **(Clenoid Foss)** & **(Clenoid Foss)** & **(Clenoid Foss)** & **(Clenoid Foss)** & **(Clenoid Foss)** & **(Clenoid Foss)** & **(Clenoid Foss)** & **(Clenoid Foss)** \\ \hline \multirow{2}{*}{**EBM-Ti**} & _Zirconia_ & 11.0718 & 0.05815 & 10.17489 & 0.05897 & 0.00260 & 0.02600 & 0.92190 & 0.09404 & -0.05234 & 0.05573 \\  & _Acyclic_ & 10.19723 & 0.05823 & 10.19183 & 0.05898 & 0.00090 & 0.02600 & 0.97300 & 0.03440 & -0.05412 & 0.05592 \\ \hline \multirow{2}{*}{**Zirconia**} & _Zirconia_ & 11.84253 & 0.06861 & 11.84219 & 0.06868 & 0.00034 & 0.03100 & 0.99130 & 0.01115 & -0.06374 & 0.06442 \\  & _EBM-Ti_ & 11.70935 & 0.06783 & 11.70907 & 0.06790 & 0.00028 & 0.03000 & 0.99278 & 0.00918 & -0.06380 & 0.06436 \\  & _Acyclic_ & 11.75028 & 0.06807 & 11.75010 & 0.06814 & 0.00018 & 0.03000 & 0.99530 & 0.00590 & -0.06831 & 0.06417 \\ \hline \multirow{2}{*}{**Acyclic**} & _Acyclic_ & 5.29207 & 0.06205 & 5.29115 & 0.07802 & 0.00092 & 0.03200 & 0.97000 & 0.02920 & -0.06331 & 0.06175 \\  & _EBM-Ti_ & 5.30092 & 0.06215 & 5.29993 & 0.07814 & 0.00153 & 0.03200 & 0.96190 & 0.04850 & -0.06840 & 0.06766 \\  & _Zirconia_ & 5.28628 & 0.06198 & 5.28463 & 0.07793 & 0.00165 & 0.03100 & 0.95880 & 0.05240 & -0.06450 & 0.06780 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Mean (\(\pm\) standard deviation) of the pre-test and post-test weights among the condylar parts of different materials tested against different glenoid fossa materials, along with paired t-test values.
# Text-Based Emotion Recognition in English and Polish for Therapeutic Chatbot
## 2 Related Work
### Text-Based Sentiment and Emotion Recognition
There were only four classes of emotion (_Happy_, _Sad_, _Angry_, and _Others_) and three different classification subtasks. Among the top systems, there were again examples of models leveraging both vector representations of sentences and emotion-related features. Even though there are many English-language corpora of emotional texts, for example, References [13; 17; 25], only a few relevant resources exist for Polish. Until recently, the majority of approaches to sentiment analysis in Polish were based on lexicons, such as _plWordNet 4.0 Emo_[26; 27] or the _Nencki Affective Word List (NAWL)_[28]. In recent years, several sentiment-labeled corpora have also been created. One is _PolEmo_[29], a corpus of consumer reviews from four domains: medicine, hotels, products, and schools. It contains 8216 reviews having 57,466 sentences. The corpus is labeled, both on review and sentence level, with four polarity classes: positive, neutral, negative, and ambiguous. Another example is the HateSpeech corpus [30], the current version of which contains over 2000 manually annotated posts crawled from the Polish public web. The posts contain various types and degrees of offensive language, expressed toward minorities (e.g., ethnic, racial). However, to the best of our knowledge, no emotion-labeled corpora exist for Polish. In addition, the current corpora contain no dialogue phrases. Our work aims to fill these gaps by creating a new corpus, suitable for the context of a chatbot.
###### Abstract
In general, the knowledge-driven model is more subjective while the data-driven model is relatively objective and can accurately reflect the correlation between landslide susceptibility and its basic environmental factors. However, the data-driven model is complex in the model training and testing process and requires a large number of training samples. Both types of models can get a more reasonable result, however; they mainly provide information on the probability of landslide occurrence, but no information on the size of a potential landslide once occurring. In other words, the landslide susceptibility map applied by those methods only reflected the possibility of landslide occurrence, while it cannot express the probability of a landslide. Research has shown that there is a significant difference in spatial variation between the susceptibility and erosion risk of a mass movement in the watershed [21]. Thus, the results only consider landslide frequency as incomplete for the decision-makers to implement land planning or other policy in the region. Therefore, it will also be meaningful to understand the sediment yield and prevent soil erosion in the catchment when both considering the scale and the frequency of the landslide. If the volume of the landslide is considered in the assessment of landslide susceptibility, it is necessary to choose a suitable method. Whatever the relationship between landslide and the landslide-related factors, it can be inferred from the relationship between landslide and the landslide-related factors that had occurred. Thus, volume ratio (VR), which reflected the relationship between the volume of the landslide and each landslide conditioning factors, is used in the study, and the revised increase-rate-analysis method (RIRA) method is also a suitable way for quantitative analysis of the relationship between landslide scale and various factors. The RIRA method is an improvement on the increase-rate-analysis method (IRA), which is used to evaluate the sensitivity of the landslide to a single variable [1]. On this basis, [PERSON] [22] has proposed a revised increase-rate-analysis method (RIRA) based on the importance ranking of influencing factors and sensitivity analysis for a large number of data-influencing factors. The method has been successfully applied in the analysis of the influencing factors of the silt dam conditions in the Culiacan basin in northwestern Mexico. Recently, as a basic analysis tool for landslide hazards,ArcGIS can effectively perform spatial data management and manipulation for the analysis and has been used in many studies [16]. It is also an indispensable tool for landslide susceptibility mapping. The objective of this study included: (a) applying the VR model, the RIRA method, and GIS in the spatial event prediction based on considering landslide scale; (b) creating a landslide susceptibility map for the Yangou small watershed on the Loess Plateau of China; (c) assessing the landslide susceptibility and providing suggestions for landslide prevention and soil erosion of the small catchment.
## 3 Results
### Experimental Results
#### 4.1.1 Comparison with Other Classification Models  
The metrics for comparison were calculated based on the formulae presented in Table 3. The effectiveness of the classifiers was evaluated through several criteria: accuracy (Acy), sensitivity (Sny) or recall, specificity (Spy), precision (Prn), and the F1 score. These metrics were derived for several classifiers including a Support Vector Machine (SVM), a Light Gradient-Boosting (LGBM) model, and an ensemble SVM and LGBM model. Table 5 displays the performance metrics for these classifiers, highlighting that our proposed model achieved the highest accuracy of 98.13%. Moreover, it outperformed in all other evaluative measures, indicating a superior performance across the board.