esa-sceva/satcom-chunk-collection huggingface dataset:
# Antiproliferative Activity of Aminobenzylnaphthols Deriving from the Betti Reaction
## 1 Introduction
synthesized 2-aminomethylated estrone and estradiol derivatives with the Betti protocol [24] and tested them against gynecological tumor cell lines. They showed an antiproliferative activity that is better than or comparable with the original estrone or estradiol. However, the tested compounds had a better water solubility in comparison with the starting materials, thus emerging as a new potential clinical candidate. [PERSON] et al. prepared four new compounds starting with 4-hydroxycoumarin and using tyramine, octopamine, norepinephrine, or 3-methoxyytramine as the amine reagent in the Betti reaction [25]. In vitro cytotoxicity experiments were performed against the cell lines of adenocarcinoma, human colon and breast cancer, and, finally, against lung fibroblast lines. A significant selectivity towards colorectal carcinoma cells was observed [25]. New molecules containing both the benzothiazole and the pyrazole moieties were synthesized and evaluated for their cytotoxic activity against some human cell lines. Their action was connected to DNA binding, causing the inhibition of topoisomerase I [26]. The inhibition of YAP-TEAD complexes is a new chemotherapeutic strategy [27]. New compounds obtained with the Betti reaction were investigated to implement this strategy [28]. Finally, phthalocyanines modified with Betti bases were employed in DNA cleavage experiments as a preliminary test for future clinical applications [29], and interesting result were recorded. A recent interest has emerged in the employment of amino acid derivatives as prodrugs [30]. The functionalization of a drug with an amino acid residue results in some positive outcomes, such as the improvement of the delivery of the drug to the target tissue, and the lowering of its toxicity. For example, boroxazolidones were reported to have modest antitumor properties; however, the L-valine derivatives showed strong cytotoxic effects [31]. Along the same lines, L-valine modified dimethyl-curcumin showed a great increase in anti-proliferative activity compared to the original drug, thus distinguishing itself as a potent anticancer agent [32]. At this stage, we chose molecules **1**[12] and **2**[14; 15] represented in Figure 1. Molecule **1** is a prototypal aminobenzylnaphthol obtained with the Betti reaction; this type of molecule has not been tested as an antiproliferative agent. Moreover, molecule **2** is the simplest aminobenzylnaphthol, in which the valine residue is present, an opportunity that deserves to be investigated, considering the results obtained in valine containing prodrugs. We decided to test these compounds against the already cited Caco-2 adenocarcinoma and SH-SYSY neuroblastoma cells chosen as representative test cell lines. Cisplatin was used as a standard in these tests.
## 4 Random Linear Network Coding for Mobile Video Delivery
### RLNC and Resource Allocation
It is easy to prove the aforementioned heuristic has a reduced computational complexity and when it identifies an instance of \(\mathbf{m}\) and \(\mathbf{N}\) they jointly constitute a feasible solution to GRAP. Since \(\mathbf{m}\) and \(\mathbf{N}\) are independently optimized, the heuristic solution is not always optimum. However, for realistic network and video service deployments, the heuristic provides good-quality solutions [70; 72]. It has also been observed that GRAP can be extended to system models where EW-RLNC implementations are in use [70]. In these cases, terms \(N_{1},\ldots,N_{L}\) represents the number of coded packet transmissions associated with expanding windows \(\mathbf{w_{1}},\ldots,\mathbf{w_{L}}\) to be scheduled for transmission. Similarly, terms \(m_{1},\ldots,m_{L}\) identifies the MCSs to be used for transmissions associated with \(\mathbf{w_{1}},\ldots,\mathbf{w_{L}}\) respectively. As observed in Section 2.3, a user can decode the first \(\ell\) video layers if it successfully recovers any of the expanding windows \(\mathbf{w_{\ell}},\mathbf{w_{\ell}}+\mathbf{1},\ldots,\mathbf{w_{L}}\). As such, in this case, the probability of a user successfully recovering the first \(\ell\) video layers is \(\forall_{i=\ell}^{L}\ P_{d,\mathbf{w_{i}}}(\mathbf{N})\), where \(P_{d,\mathbf{w_{i}}}(\mathbf{N})\) is the probability of a user recovering \(\mathbf{w_{i}}\) - thus terms \(\mathcal{U}_{1},\ldots,\mathcal{U}_{L}\) have to be redefined accordingly. Despite its new setting, the aforementioned two-step heuristic can still be applied to find good-quality feasible solution to GRAP [70].
## 7 Results
### Performance on the Zipf's Tail  
Word frequencies can be modeled reasonably well by a Zipf distribution. It is a very heavy-tailed distribution, where there is a vast number of words with low frequencies. The abundance of such words is a challenge for most learning systems, as the data for these points is sparse. Our question is, how hard are these words for our trained models? We grouped words that were in our testing set by their frequencies in the training set. The resulting word groups are:  
* Unseen: present in the test but not in train data;
* \([1,100]\): words appearing in the training set from 1 to 100 times;
* [101, 10,000]: words appearing in training set from 101 to 10,000 times. Alpha-word accuracy results for these groups are shown in Table 6. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multirow{2}{*}{**Language**} & \multirow{2}{*}{**Raw**} & \multirow{2}{*}{**Hungpell**} & \multirow{2}{*}{**Dict.**} & \multicolumn{2}{c}{**ByT5**} & \multicolumn{2}{c}{**Dict.+ByT5**} \\ \cline{5-8}  & & & & \multicolumn{2}{c}{**\#samples: \(\times\)1**} & \(\times\)**19** & \(\times\)**1** & \(\times\)**19** \\ \hline Croatian & 64.05 & 66.43 & 74.06 & 90.27 & 96.71 & \\ Czech & 38.68 & 40.15 & 71.37 & 89.88 & 94.52 & \\ French & 60.81 & 64.94 & 70.87 & 93.45 & 96.52 & \\ Hungarian & 38.16 & 46.35 & 69.84 & 88.31 & 93.96 & 94.31 & 96.85 \\ Irish & 53.49 & 56.01 & 73.16 & 89.48 & 94.49 & \\ Latvian & 37.69 & 44.21 & 66.29 & 88.88 & 93.01 & \\ Lithuanian & 44.78 & 44.87 & 68.44 & 89.68 & 94.19 & 94.70 & 96.73 \\ Polish & 49.10 & 56.61 & 70.02 & 91.38 & 96.76 & \\ Romanian & 51.93 & 54.54 & 70.29 & 90.50 & 94.14 & \\ Slovak & 43.92 & 48.59 & 72.89 & 91.05 & 95.56 & \\ Spanish & 64.07 & 68.03 & 71.58 & 93.12 & 95.98 & \\ Turkish & 51.18 & 51.69 & 72.58 & 90.00 & 95.29 & \\ Vietnamese & 11.92 & 11.84 & 56.19 & 87.34 & 93.40 & 87.86 & 93.77 \\ \hline Average & 46.91 & 50.33 & 71.89 & 90.26 & 94.60 & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Alpha-word accuracies for the simultaneous diacritics and typographic error corrections. A substantial part of errors come from the words that are unseen during the training. Excluding Vietnamese and Irish, this ranges from 13% (Spanish, French) to 36% for Slovak. The Vietnamese outlier of 1% may be due to its linguistic nature, while the Irish outlier of 46% is due to its very small dataset. Overall, the smaller the dataset (Table 1), the more unseen or rare words, and the associated errors, we have. Similar to the Dictionary method and the other classical methods, unseen data is also a significant source of errors for the transformer model. Different to the classical approaches, however, is the transformer model, which is based on neural networks, and it can generalize to unseen data. To investigate this generalization, we filtered all the words that were in the testing set and not in the training and calculated the percentages, as is shown in Table 7. We can see that the ByT5 model successfully restores more than 76% of unseen words for each language.
# Storytelling with Image Data: A Systematic Review and Comparative Analysis of Methods and Tools
## 4 Task 2. Story Modeling
### Organizing and Summarizing Feature Data
#### 4.1.2 Clustering  
Sometimes the metadata we use do not belong to specific categories; however, different metadata groups have similar features and could be clustered into a single group. Several existing clustering methods fit different data types. In this section, some of the most useful ones are described [67].
* **Representative-Based Methods.** These methods are the most straightforward clustering techniques, since they use the similarity or distance of the data points in the feature space. In these methods, candidates are chosen among either the data points or a function of the data points, such as mean or median, to be the center of the clusters. The most popular techniques in this category are K-Means, K-Median, and K-Medoids.
* **Hierarchical Clustering Methods.** Hierarchical clustering methods cluster the data on different levels. In the hierarchical clustering approach, a tree-shaped clustering is produced so that each tree level demonstrates one option for clustering with a specific number of clusters. e.g., the first node of the tree represents all data points in one cluster, and the leaf nodes represent clustering with C clusters, in which C is the number of data points. This approach is advantageous when we need different insights from the clustering and we could obtain those insights by looking at each tree level. Bottom-Up (Agglomerative) and Top-Down (Divisive) are two types of hierarchical clustering approaches.
* **Probabilistic Methods.** In the probabilistic approaches, for each data point, C likelihoods will be calculated, in which C is equal to or less than the number of the clusters. In these approaches, each data point could belong to multiple clusters but with different probabilities. This method is called soft clustering.
* **Density-Based Methods.** The problem with distance-based and probabilistic clustering is that the shapes of the clusters are always defined based on the model we are using, e.g., the K-means technique's cluster shape is always spherical. However, sometimes the clusters of our data points have multiple odd shapes, and the clusters are too close to each other. In this scenario, none of the above functions can correctly cluster them. Density-based clustering solves this problem by identifying fine-grained dense regions.
# Flexible DEM Model Development and Parameter Calibration for Rape Stem
## 2 Materials and Methods
### Calibration of Flexible Discrete Element Parameter for Rape Stem
#### 2.4.2 Three-Point Bending Test  
The three-point bending test could quantify and comprehend the elastoplastic flexural behavior of crop stalk [23]. In this research, the maximum damage force of the rape stems was obtained by the three-point bending test, which was used to calibrate the bonding parameters of the flexible discrete element model.  
The test device is shown in Figure 8a. The loading device was the electronic universal testing machine (utm6503, Suns, Shenzhen, China). The test materials were selected from rape stems with an average diameter of 7.0 mm (with 95% confidence interval lower and upper limits of 6.7 mm and 7.4 mm, respectively). The moisture content of the rape stems was measured to be 51.3%. The distance between the support points on both sides of the sample was 60.0 mm, and the loading speed was 1 mm/s. The support cylinder and loading cylinder radii were 4.0 mm. The rape stem was extruded and deformed under the load until broken, and the maximum bending-damage force was recorded. The test was repeated 5 times and averaged, resulting in a mean maximum damage force of 16.20 N and a coefficient of variation of 4.94%.
## 5 Conclusions  
In the present study, a QSAR-MLR model was developed by using molecular descriptors calculated with the Dragon software, which adequately predicts the binding affinity for DAT. The model developed with the QSARINS software fulfilled all regulatory principles established by the OECD. The robustness of the model was tested through several internal validation techniques (LOO, LMO, and _Y-scrambling_), and its predictability was tested using a set of external predictions (external validation). The developed model was used to perform a virtual screening of compounds with different actions on the CNS. The model predicts four compounds with LogK\({}_{\text{d}}\) values lower than the \({}^{123}\)I-Ioflupane value; these compounds should present good binding affinities for DAT. The docking experiments corroborate the results of the model prediction and give information about the main interactions of these compounds with the active site. The combination of both strategies strengthens the probabilities of identifying new compounds with potential affinity for DAT. In conclusion, the proposed computational tool is efficient in the identification of novel DAT radiotracers.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app15158134/s1](https://www.mdpi.com/article/10.3390/app15158134/s1)]([https://www.mdpi.com/article/10.3390/app15158134/s1](https://www.mdpi.com/article/10.3390/app15158134/s1)).  
Conceptualization, J.A.C.-G., M.S.-M., and F.P.-G.; methodology, M.S.-M. and K.M.-U.; software, K.M.-U.; validation, M.R.-O. and V.P.-D.; formal analysis, V.P.-D.; investigation, J.A.C.-G. and K.M.-U.; resources, M.R.-O.; data curation, M.S.-M. and V.P.-D.; writing--original draft preparation, J.A.C.-G. and M.S.-M.; writing--review and editing, J.A.C.-G., F.P.-G., and F.T.; visualization, F.P.-G.; supervision, F.T. and F.P.-G. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
All the data required to reproduce the results of this paper can be found directly in the main text (Table 1 and Figures 1-3).  
The authors are grateful to [PERSON] and her group of the University of Insubria for kindly providing the QSARINS software for this study. [PERSON], [PERSON] thanks the program 'Estades Temporals per a Investigators Convidats' for a fellowship to work at Valencia University in 2018. [PERSON] thanks the Universitat de Valencia for the Special Research Actions Funding 2024. Powered@NLHPC: This research was partially supported by the supercomputing infrastructure of the NLHPC (ECM-02) of the Universidad de Chile.  
The authors declare no conflicts of interest.
# Mechanical and Acoustic Emission (AE) Characteristics of Rocks under Biaxial Confinements
## 4 Strength Criterion  
Drucker-Prager (D-P) criterion [55] is an extension of the Mohr-Coulomb criterion [56], which accounts for the effects of all principal stresses and overcomes the main defect of underestimating the intermediate principal stress of the Mohr-Coulomb criterion. The generalized form is written as:  
\[\alpha I_{1}+J_{2}^{1/2}=k, \tag{4}\]  
where \(I_{1}\) and \(J_{2}\) are the first and second stress invariants, and \(\alpha\) and \(k\) are material constants of the rock, respectively.  
\[I_{1}=\sigma_{1}+\sigma_{2}+\sigma_{3}, \tag{5}\]  
\[J_{2}=\frac{1}{6}\Big{[}(\sigma_{1}-\sigma_{2})^{2}+(\sigma_{2}-\sigma_{3})^{ 2}+(\sigma_{3}-\sigma_{1})^{2}\Big{]}. \tag{6}\]  
For the outer apices-circumscribed D-P criterion (ODP):  
\[\alpha=\frac{2\sin\varphi}{\sqrt{3}(3-\sin\varphi)}, \tag{7}\]  
\[k=\frac{6c\cos\varphi}{\sqrt{3}(3-\sin\varphi)}. \tag{8}\]  
For the inner apices-inscribed D-P criterion (IDP):  
\[\alpha=\frac{2\sin\varphi}{\sqrt{3}(3+\sin\varphi)}, \tag{9}\]  
\[k=\frac{6c\cos\varphi}{\sqrt{3}(3+\sin\varphi)}. \tag{10}\]  
Drucker-Prager failure criterion for biaxial confinements condition is achieved by setting \(\sigma_{3}=0\), so the Equation (4) can be written as:  
\[\alpha(\sigma_{1}+\sigma_{2})+\sqrt{\frac{\sigma_{1}^{2}-\sigma_{1}\sigma_{2} +\sigma_{2}^{2}}{3}}=k. \tag{11}\]  
The cohesions \(c\) for granite, marble and sandstone were calculated according to triaxial compression data with \(\sigma_{2}=\sigma_{3}\)[57] to be 50.32, 24.1, and 20.54 MPa, respectively. The internal frictional angles \(\varphi\) were 37\({}^{\circ}\), 32\({}^{\circ}\), and 29\({}^{\circ}\), respectively. In Figure 16, failure stress data is plotted against two theoretical solutions (ODP and IDP), indicating that the outer apices-circumscribed Drucker-Prager criterion overestimates the \(\sigma_{\rm{bcs}}\) of the three types of rocks. The inner apices-inscribed Drucker-Prager criterion can accurately predict the \(\sigma_{\rm{bcs}}\) of granite and marble when \(\sigma_{2}\) is small (\(\leq\)20 MPa for granite and \(\leq\)10 MPa for marble),whereas it loses accuracy under high \(\sigma_{2}\) value. As for sandstone, OPD and IPD both overestimate the \(\sigma_{\rm{bos}}\), and the gap between the lab data and the theoretical data is larger than that of granite and marble.
## 4 Experiments
### Quantitative Experimental Results
By combining the predicted pixel categories and instance semantics to generate a classification mask, more advanced semantic information can be provided, enabling the model to better understand and distinguish the segmented objects in the open-pit mining area in the image. SegFormer's recall reaches 85.56%, implying that SegFormer has high integrity in generating CNN-like multi-level features to represent the target object through the hierarchical Transformer encoder, which is conducive to the model's segmentation of the open-pit mining area in the presence of complex backgrounds. SegMine integrates the advantages of Transformer and CNN, injects multi-scale priori information into global semantic features, and integrates multi-layer global semantic information to guide pixel classification by adding an attention mask module. The mIoU of SegMine reaches 86.91%, which is the best performance for this indicator, indicating that the segmentation results of SegMine are more consistent with the real labeling. Additionally, it significantly outperforms other models in precision and dice coefficients. This indicates that the improved encoder can enhance the model's ability to acquire local spatial detail information, further addressing the problems of disappearing small-scale target features and insufficient information expression. The proposed decoder integrates multi-layer similarity mapping to obtain a more semantically informative segmentation mask and thereby alleviate the problem of low segmentation accuracy due to blurring of the boundaries of the open-pit mining area caused by feature upsampling. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **mIoU (\%)** & **Precision (\%)** & **Recall (\%)** & **Dice (\%)** \\ \hline
**Segmenter**[38] & 85.68 & 85.29 & 84.11 & 90.32 \\
**RtFormer**[39] & 84.23 & 88.85 & 81.54 & 90.59 \\
**SegFormer**[40] & 85.56 & 85.95 & **85.56** & 90.60 \\
**TopFormer**[41] & 85.53 & 88.77 & 84.94 & 91.55 \\
**Mask2 Former**[42] & 86.12 & 84.61 & 82.68 & 90.57 \\
**ViT-Adapter**[43] & 85.78 & 85.63 & 83.07 & 90.69 \\
**SegMine** & **86.91** & **89.90** & 85.33 & **92.27** \\ \hline \hline \end{tabular}  
* The bolded data indicating the optimal results.  
\end{table} 
Table 1: Performance comparison of different Transformer models.
# Design and Evaluation of a New Fuzzy Control Algorithm Applied to a Manipulator Robot
## 8 Conclusions  
Several authors agree that the use of a controller based on fuzzy logic is a very good choice for controlling non-linear systems with mathematical models difficult to represent. This factor, along with the structure of these controllers and the possibility of selecting the membership functions as well as the ranges of the fuzzy sets to be used, yields a wide variety of results that allows for improving the performance of the system to be controlled.  
In this work, we developed a fuzzy logic application for controlling the trajectory-tracking of the joints of a 2-DoF planar manipulator robot. The results of the three test trajectories have evidenced that the controllers developed satisfactorily meet the tracking control requirements. Nevertheless, the performance of the fuzzy logic controllers is visibly superior to the performance of the classic PID controller. The values of these indicators reveal that the three tested trajectories are less accurate with the PID controller. However, trajectory number three is much harder to follow, since the joints of the manipulator robot are forced to change the direction of their motion to 90\({}^{\circ}\) angles in very short time lapses.  
The main contribution of this study was the incorporation of the acceleration fuzzy variable, which allowed for noticeably improving system response. In total, we worked with three variables: position, speed and acceleration, each of them with its respective membership functions and fuzzy sets.  
The design and analyses of fuzzy controllers for manipulator robots are envisaged as future works. This should incorporate, in addition to the joint acceleration variable, the error integral variable with adaptive reset in order to improve the transient and stationary behavior in the presence of load disturbances in the second joint. Furthermore, the dynamic model of the manipulator robot could be improved by adding the Stribeck effect through modeling with a first-order non-linear differential equation.  
Conceptualization, C.U., J.K. and J.A.; Methodology, C.U., J.K. and J.A.; Software, C.U., J.K. and J.A.; Validation, C.U., J.K. and J.-A.; Formal analysis, C.U., J.K. and J.A.; Investigation, C.U., J.K. and J.A.; Resources, C.U., J.K. and J.A.; Data curation, C.U., J.K. and J.A.; Writing--original draft preparation, C.U., J.K. and J.A.; Writing--review and editing, C.U. and J.K.; Visualization, C.U. and J.K.; Supervision, C.U.; Project administration, C.U.; Funding acquisition, C.U. All authors have read and agreed to the published version of the manuscript. This research received no funding.  
This work was supported by the Vicerrectoria de Investigacion, Desarrollo e Innovacion of the Universidad de Santiago de Chile, Chile.  
The authors declare no conflict of interest.  
Figure 18: Performance indicators: fuzzy with acceleration vs. fuzzy without acceleration.
## 4 Numerical Simulation
### Numerical Results  
#### 4.2.1 Different HDRs  
The numerical simulation aligns closely with the experimental results to a high degree as shown in Figure 10. Figure 11 shows the damage morphology and fracture development of the PFC2D simulated specimens across different HDRs at the same LR of 0.8 mm\(\cdot\)min\({}^{-1}\). The damage mode and internal crack development morphology varies somewhat among the specimens with different HDRs. Notably, the number of primitive cracks within the specimen gradually reduces as the HDR decreases. However, for the HDR from 0.6 to 1, the number of cracks formed inside the specimens at the moment of damage is 698, 789 and 696, respectively. This is significantly higher than those for a HDR of 1.2 and 1.4--502 and 530, respectively (as shown in Figure 12a). Meanwhile, it can be seen that internal cracks running along the diagonal direction occur in the direction of shear damage when the HDR is between 1.6 and 2; the internal crack expansion direction becomes smaller when the HDR is between 1.2 and 1.4; however, with the HDR between 0.6 and 1, the specimen shows an end damage mode internally, with cracks on the upper and lower end faces coming together in a triangular shape towards the center, and the specimen shows the \"X-conjugate\" damage pattern.  
Figure 10: Comparison of experimental and numerical simulations of stress–strain curves.