esa-sceva/satcom-chunk-collection huggingface dataset:
# Responses of Laterally Loaded Single Piles Subjected to Various Loading Rates in a Poroelastic Soil
## 4 Parametric Study on Pile Cap Geometry  
The pile cap is part of an important element of pile foundations, in particular, for laterally loaded piles. Studies on the contribution of the pile cap to the integrity of soil/pile interaction without considering the transient fluid flow conditions have been undertaken by various researchers. The effect of pile cap geometry, pile cap width, pile cap embedment depth, and pile length, on the response of laterally loaded single piles is evaluated here. The length of the pile used was 18.0 m, while the fully embedded pile cap was 1.0 m wide by 0.3 m thick. As in the previous section, three types of loading condition were considered: (i) undrained (loading rate = 2 kN/s), (ii) partially drained (loading rate = 2 kN/h), and (iii) drained (2 kN/d); for comparison purposes, a further loading condition, i.e., (iv) drained loading in dry soil (2 kN/step) was also conducted. All the analyses were terminated at the end of the incremental loading, i.e., when the total load of 200 kN was first attained, thus the effect of consolidation on the responses of the laterally loaded single piles is not taken into consideration. The results were evaluated in terms of lateral displacement and the lateral loading of the pile.
# A Message Passing Approach to Biomedical Relation Classification for Drug-Drug Interactions
## 2 Materials and Methods
### DDI Knowledge Graph  
In order to model the DDI-specific Knowledge Graph, we used the RDF standard to create the proposed schema for representing the corpus knowledge. Figure 2 provides an overview of the DDI Knowledge Graph.  
According to the RDF principles, the base of a knowledge graph is composed of a set of <Subject, Predicate, Object> statements, with the Subject and Object resources being respectively the initial and terminal nodes of a directed edge. The Predicate resource is considered the label of the edge in question, which is a property that associates the individual resources or serves to assign a value (Object) to some attribute of the Subject. This basic model is extended by defining classes into which the objects of the world belong.  
Figure 1: An overview of our proposed architecture for the classification of DDIs.  
In the DDI corpus, all drug entities are annotated by providing the exact drug name and the location in the context of the specific sentence they are found in. Initially, each sentence of a document becomes an instance of the _Sentence_ class, with its text preserved intact in the graph as an attribute of the specific node. Additionally, a sentence refers to a set of drugs that are modeled by the _Token_ class, which is associated with the _Sentence_ through the _contains_token property_. This property has a minimum cardinality of 2, filtering sentences that mention less than two drug entities (i.e., do not contain at least one drug pair). Furthermore, the set of unique drug entities in the collection is described by the _Drug_Class_ class and its subclasses are the four types of drugs, which are mutually exclusive.  
Finally, the concept of interaction (relationship between two drugs) is also modeled through classes. Typically an RDF statement represents a binary relationship between two resources in the form of a triplet. However, it may be necessary to add additional information regarding the statement resulting in an n-ary relationship. Each possible drug pair of a particular sentence is represented by an _Interaction_ helper node. Thus, the information defining an interaction is composed centered on this node, through properties that associate it with other entities (e.g., 1 Sentence instance, 2 Drug_Class, 2 Token). Similar to the _Drug_Class_, its subclasses are based on the five predefined interaction types.  
Collectively, a drug entity (Drug_Class), referred to as (found_as) Token in a particular sentence (Sentence), participates (interaction_found) in some pairs of interactions (Interaction). The sentence contains (contains_interaction) the interacting pair, while the Token reference participates (in_interaction) in it.
# Fault Detection in Power Transformers Using Frequency Response Analysis and Machine Learning Models
## 3 Methodology
### Machine Learning Algorithms for Fault Detection
#### 3.2.7 Performance Analysis and Dataset Complexity  
The high classification accuracy (80-100%) achieved by the Decision Tree and Subspace KNN models suggests that the dataset used in this study may be relatively simple or highly structured. While these models are effective for pattern recognition, their performance can be influenced by data separability and preprocessing techniques. A key factor in assessing model reliability is the statistical complexity of the dataset, including class distributions, feature variations, and potential data imbalances.  
To ensure a comprehensive evaluation, confusion matrices were analyzed to identify false positives and false negatives, particularly in challenging fault categories such as contamination, which exhibited a 100% False Negative Rate (FNR). This suggests that while the model is highly effective for certain faults, it struggles with others, highlighting the need for improved feature extraction techniques. Furthermore, traditional accuracy metrics alone are insufficient to assess real-world applicability. Instead, precision, recall, and F1-scores offer a more balanced view of classification effectiveness, especially in cases of class imbalance.  
Overfitting risks were also considered, given that Decision Trees are prone to memorizing training data rather than generalizing well. To mitigate this, cross-validation techniques were employed to assess model stability across different test sets. The results indicate that while the classifiers perform well within the given dataset, their ability to handle unseen data remains a critical consideration. Future work should explore more advanced machine  
\begin{table}
\begin{tabular}{l l} \hline \hline
**KNN Type** & **Accuracy (\%)** \\ \hline Weighted KNN & \(\sim\)95 \\ Cubic KNN & \(\sim\)95 \\ Cosine KNN & \(\sim\)80 \\ Coarse KNN & \(\sim\)40 \\ Medium KNN & \(\sim\)95 \\ Fine KNN & \(\sim\)95 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Bar graph showing the accuracy in % form for different KNN models.  
learning models, such as ensemble methods or deep learning approaches, to enhance predictive robustness and generalization.
# Fast and Interactive Positioning of Proteins within Membranes
## 3 Results
### Comparison with Molecular Dynamics Data from Four Membrane Proteins  
We examined a sample of four membrane proteins for which we had access to molecular dynamics simulations to further evaluate our implementation. Our dataset includes two beta-barrel membrane proteins, OmpT [34] and OmpX [35]; an ion-channel with an alpha-helical transmembrane domain, Glic [36]; and a G-protein coupled receptor, BLT2 [37]. Figure 13 shows the time series of insertion depth and insertion angle compared to the values predicted by our implementation of the IMPALA approach. The results highlight the intrinsic fluctuations characteristic of molecular dynamics simulations of membrane systems. In some cases, very good agreement is obtained, such as for the Glic or BLT2 insertion angles, while in other cases, such as for the Glic insertion depth, discrepancies are evident. In this particular case, this observation could be due to the thicker POPC membrane used in this simulation, e.g., compared to DMPC. The same argument may hold for the BLT2 insertion depth, as an even thicker PEA membrane was used. For the Omp simulations, it is obvious that at least OmpT is not fully stabilized with respect to membrane insertion, which generally suggests that simulations on the order of 10 s of nanoseconds are not sufficient to unambiguously assess membrane insertion. This is further confirmed by an alternative MD dataset extracted from the MemProtMD database [7] by analyzing the last snapshot deposited. Our IMPALA predictions are in good agreement with this data. For OmpX, our insertion depth is between the database value of +9.6 A and the MD time series shown, which fluctuates around 0 A, while there is good agreement for the insertion angle of both MD datasets, which is about 10 degrees higher than in our calculation. For Glic, the MemProtMD data are coherent and close to the IMPALA predictions. No reference data for BLT2 exist in MemProtMD.  
Figure 13: Insertion depths and angles for molecular dynamics simulations of (**A**) OmpT, (**B**) OmpX, (**C**) Glic, and (**D**) BLT2 with total lengths of 10, 15, 300, and 500 ns, respectively. For Glic and BLT2, only the last 50 ns of the simulation are shown. The optimized IMPALA insertion depth and angle are superposed as black line onto the molecular dynamics time series, with indication of an estimated minimal error of \(\pm 1\) Å and \(\pm 2\) degrees. We added green arrows to indicate values at the end of a set of alternative MD simulations deposited in the MemProtMD database [7] for comparison.
## Appendix A  
_Appendix A.1. The Procedure to Evaluate the Adequate Equation of the \(y_{\text{28D}}\) Response (28-Day Compression) [30]_  
The results for the complete model with regression analysis are as follows:  
\[1.\;\text{y28}_{\text{D}}=36.213+0.059\text{x}_{1}-0.343\text{x}_{2}-0.202 \text{x}_{1}{}^{2}-0.0157\text{x}_{2}{}^{2}+0.00147\text{x}_{1}\text{x}_{2}\] (A1)  
\[\text{(1.115)}\quad\text{($-1.414)}\quad\text{($-1.205)}\quad\text{($-1.498)} \quad\text{(0.0422)}\]  
\[\text{R}^{2}=0.928,\;\text{R}^{2}_{\text{adj}}=0.877,\;\text{s}=0.1.739,\; \text{PRESS}=121.931,\]  
The normality test is passed (\(p=0.397\)), and the constant variance test is passed (\(p=0.504\). Delete the \(\text{x}_{1}\text{x}_{2}\) variable and recompute:  
\[2.\;\text{y28}_{\text{D}}=36.176+1.074\text{x}_{1}-0.339\text{x}_{2}-0.202 \text{x}_{1}{}^{2}-0.0157\text{x}_{2}{}^{2}\] (A2)  
\[\text{(1.299)}\quad\text{($-1.641)}\quad\text{($-1.288)}\quad\text{($-1.69$)}\]  
\[\text{R}^{2}=0.928,\;\text{R}^{2}_{\text{adj}}=0.892,\;\text{s}=1.627,\; \text{PRESS}=7.751\]  
Delete the \(\text{x}_{1}{}^{2}\) variable and recalculate:  
\[3.\;\text{y28}_{\text{D}}=36.596+0.065\text{x}_{1}-0.243\text{x}_{2}-0.0205 \text{x}_{2}{}^{2}\] (A3)  
\[\text{(0.238)}\quad\text{($-1.217)}\quad\text{($-2.184$)}\]  
\[\text{R}^{2}=0.913,\;\text{R}^{2}_{\text{adj}}=0.884,\;\text{s}=1.686,\; \text{PRESS}=4.874\]  
Delete the \(\text{x}_{1}\) variable and recalculate:  
\[4.\;\text{y28}_{\text{D}}=36.760-0.243\text{x}_{2}-0.0205\text{x}^{2}\] (A4)  
\[\text{($-1.279$)}\quad\text{($-2.295$)}\]  
\[\text{R}^{2}=0.13,\;\text{R}^{2}_{\text{adj}}=0.895,\;\text{s}=1.604,\; \text{PRESS}=4.544\]  
The normality test is passed (\(p=0.406\)), and the constant variance test is passed (\(p=0.378\)). No influential data point was found.  
\[(-0.719)\quad(-0.355)\quad(2.200)\quad(2.132)\]  
\[\text{R}^{2}=0.241,\,\text{R}^{2}_{\text{adj}}=0.209,\,\text{s}=0.321,\,\text{ PRESS}=2.856.\]  
Although the linear terms of \(\text{x}_{1}\) and \(\text{x}_{3}\) are insignificant (\(p>0.05\)), the quadratic terms \(\text{x}_{2}{}^{2}\) and \(\text{x}_{3}{}^{2}\) are significant in the model. So, the linear terms of \(\text{x}_{1}\) and \(\text{x}_{3}\) are hierarchically added to the quadratic equation.  
The normality test is passed (\(p=0.130\)), and the constant variance test is failed (\(p=0.246\)). One influential data point was found in the sixth run, \(\text{t}_{\text{i}}=-2.346\).
## 4 Discussion on Regional Dust Events
### Dust Outbreaks in Europe
CHIMERE overestimates the background concentrations (before the dust event) but the timing of the dust event incidence is again rather well captured with an increase of concentrations of particles with diameter above 2.5 \(\upmu\)m. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Observations**} & \multicolumn{3}{c}{**Model**} \\ \cline{2-7}
**Date** & \multicolumn{2}{c}{\(\mathbf{\mu_{g}\,m^{-3}}\)} & \multicolumn{1}{c}{**\%**} & \multicolumn{2}{c}{\(\mathbf{\mu_{g}\,m^{-3}}\)} & \multicolumn{1}{c}{**\%**} \\ \cline{2-7}
**PM\({}_{\mathbf{2.5}}\)** & **PM\({}_{\mathbf{10}}\)** & **PM\({}_{\mathbf{2.5}}\)/PM\({}_{\mathbf{10}}\)** & **PM\({}_{\mathbf{2.5}}\)** & **PM\({}_{\mathbf{10}}\)** & **PM\({}_{\mathbf{2.5}}\)/PM\({}_{\mathbf{10}}\)** \\ \hline
26 March & 6.5 & 11 & 60 & 21 & 25 & 85 \\
27 March & 8.7 & 13 & 67 & 25 & 29 & 85 \\
28 March & 8.4 & 14 & 60 & 19 & 22 & 86 \\
29 March & 13 & 23 & 57 & 21 & 25 & 84 \\
30 March & 17 & 30 & 57 & 30 & 43 & 69 \\
31 March & 17 & 42 & 40 & 90 & 185 & 48 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Daily evolution end of March 2014 of observed and simulated concentrations at Billere (north of Pyrenees mountain range), the urban background station the closest to the Pyrenees with both PM\({}_{10}\) and PM\({}_{2.5}\) measurements. Figure 17: End of March evolution of the PM\({}_{10}\) concentrations at the Pic du Midi station (42.94\({}^{\circ}\) N–0.14\({}^{\circ}\) E, altitude: 2876 m) and particle mass size distributions (Observations versus Modeled values). Far away in the north of Europe, at the Danish station DK0012R, a small signal can be detected (Figure 13), a slight increase of calcium measurements is observed and captured by the model. According to model results the dust plume extension (Figure S10 in Supplementary Materials) is simulated up to the Arctic regions on 30-31 March, unfortunately fully trustable satellite data are difficult to use to confirm this feature. Looking at AERONET version 1.5 data (cloud screened data) which were carefully used in recent works [119] compared to version 2 data (cloud screened and quality-assured data) in Figure S13 in Supplementary Materials shows a sharp increase of the AOD on 30 March which could indicate the arrival of this dust plumes at Ittoqqrotoornii station (70.48\({}^{\circ}\) N-21.95\({}^{\circ}\) E, Greenland).
## 3 Results
### Characteristics of the Aeroelastic Model Tree
#### 3.2.2 Model Designs  
The structural design of the aeroelastic model of the decurrent tree (including trunk and branches) described by [PERSON] et al. [5] is briefly reviewed in this section.  
Based on the requirements of the similarity parameters (including length, drag coefficients, mass, bending stiffness and the size of wind tunnel), a 1:6 geometrically scaled model of the tree is selected for wind tunnel tests. The trunk model is designed as the combination of a central spine and cladding modules. A high-strength aluminum rod (\(\rho=2700\) kg/m\({}^{3}\), \(E=6.91\times 10^{10}\) Pa/m\({}^{2}\)) of a circular cross section serves as the \"spine\" of the trunk model. Based on the diameters at 15 trunk heights and elastic modulus of the trunk, the aluminum spine was machined according to the bending stiffness scale. The aerodynamic shape of the trunk was completed by 15 cladding segments affixed to the spine. In order to prevent the additional stiffness from the cladding segments, gaps of 0.8 mm are imposed between two adjacent cladding segments. By altering the wall thickness of the cladding segments, the modelled mass of the trunk was adjusted to match the mass scale. The properly scaled mass and bending stiffness ensures that the trunk model matches the trunk frequency of the prototype.  
The requirements for the branch models are similar to these for the trunk model. However, the design for the trunk is not suitable for the branches because of small diameters of branch models at the length scale. Therefore, a combination of aluminum wire (\(\rho=2700\) kg/m\({}^{3}\), \(E=6.91\times 10^{10}\) Pa/m\({}^{2}\)) and rigid hollow rods (\(\rho=930\) kg/m\({}^{3}\)) based on equivalent displacement was used as an alternative. Because the aeroelastic model and the alternative model have the same displacement under the same load, it ensures the consistency of branch streamlining and bending stiffness. The final branch mass specified by the mass scale is obtained by adjusting the wall thickness of the rigid hollow rods. The aerodynamic shape of the branches was completed by altering the diameters of the rigid hollow rods according to length scale. A total of 10 order-1 branches and 102 order-2 branches was made to model the crown frame of the prototype.  
Leaf effects on tree are reflected on dynamic characteristics of tree and crown morphology and, hence, the total leaf mass and leaf area index (LAI) were employed as key parameters for leaf cluster models. The leaf cluster model was made up of steel piano wire and plastic sheet according to the typical leaf cluster of the prototype. The model neglected the dynamic characteristics of the leaf cluster, but scaled the aerodynamic shape of the single-leaf cluster of the prototype. The excess stiffness reduces the streamlining of the modelled cluster that should be observed in the prototype in strong winds.
## Appendix A Transfer Matrices  
We list the components \(m_{ij}\)\((i,j=1,2)\) of the unit-cell transfer matrices for the mechanical systems described in Section 3.  
* Bi-layer system: \[m_{11} = \cos\phi_{1}\cos\phi_{2}-\frac{Q_{1}}{Q_{2}}\sin\phi_{1}\sin \phi_{2},\] \[m_{12} = \frac{1}{Q_{1}}\sin\phi_{1}\cos\phi_{2}+\frac{1}{Q_{2}}\cos\phi _{1}\sin\phi_{2},\] \[m_{21} = -Q_{1}\sin\phi_{1}\cos\phi_{2}-Q_{2}\cos\phi_{1}\sin\phi_{2},\] \[m_{22} = \cos\phi_{1}\cos\phi_{2}-\frac{Q_{2}}{Q_{1}}\sin\phi_{1}\sin\phi _{2};\] (11)
* Bi-layer system with oscillator in parallel: \[m_{11} = \cos\phi_{1}\cos\phi_{2}-\frac{Q_{1}}{Q_{2}}\sin\phi_{1}\sin\phi _{2}-\frac{1}{Q_{2}}\cos\phi_{1}\sin\phi_{2}\frac{m\omega^{2}\gamma}{\gamma- m\omega^{2}},\] \[m_{12} = \frac{1}{Q_{1}}\cos\phi_{2}\sin\phi_{1}+\frac{1}{Q_{2}}\cos\phi _{1}\sin\phi_{2}-\frac{\sin\phi_{1}\sin\phi_{2}}{Q_{1}Q_{2}}\frac{m\omega^{2} \gamma}{\gamma-m\omega^{2}},\] \[m_{21} = -Q_{1}\cos\phi_{2}\sin\phi_{1}-Q_{2}\cos\phi_{1}\sin\phi_{2}-\cos \phi_{1}\cos\phi_{2}\frac{m\omega^{2}\gamma}{\gamma-m\omega^{2}},\] \[m_{22} = \cos\phi_{1}\cos\phi_{2}-\frac{Q_{2}}{Q_{1}}\sin\phi_{1}\sin\phi _{2}-\frac{1}{Q_{1}}\cos\phi_{2}\sin\phi_{1}\frac{m\omega^{2}\gamma}{\gamma- m\omega^{2}};\] (12)* Bi-layer system with continuous phase in parallel: \[m_{11} = \cos\phi_{1}\cos\phi_{2}-\frac{Q_{1}}{Q_{2}}\sin\phi_{1}\sin\phi_{ 2}-\frac{Q_{3}}{Q_{2}}\cos\phi_{1}\sin\phi_{2}\tan\phi_{3},\] \[m_{12} = \frac{1}{Q_{1}}\cos\phi_{2}\sin\phi_{1}+\frac{1}{Q_{2}}\cos\phi_{1 }\sin\phi_{2}-\frac{Q_{3}}{Q_{1}Q_{2}}\sin\phi_{1}\sin\phi_{2}\tan\phi_{3},\] \[m_{21} = -Q_{1}\cos\phi_{2}\sin\phi_{1}-Q_{2}\cos\phi_{1}\sin\phi_{2}-Q_{3 }\cos\phi_{1}\cos\phi_{2}\tan\phi_{3},\] \[m_{22} = \cos\phi_{1}\cos\phi_{2}-\frac{Q_{2}}{Q_{1}}\sin\phi_{1}\sin\phi_ {2}-\frac{Q_{3}}{Q_{1}}\cos\phi_{2}\sin\phi_{1}\tan\phi_{3};\] (11)
* Bi-layer system with oscillator in series: \[m_{11} = \cos\phi_{1}\cos\phi_{2}\,\frac{\gamma-m\omega^{2}}{\gamma}- \frac{1}{Q_{2}}\cos\phi_{1}\sin\phi_{2}\,m\omega^{2}-\] \[Q_{1}\sin\phi_{1}\cos\phi_{2}\,\frac{2\gamma-m\omega^{2}}{\gamma^ {2}}-\frac{Q_{1}}{Q_{2}}\sin\phi_{1}\sin\phi_{2}\,\frac{\gamma-m\omega^{2}}{ \gamma},\] \[m_{12} = \frac{1}{Q_{1}}\sin\phi_{1}\cos\phi_{2}\,\frac{c-m\omega^{2}}{ \gamma}-\frac{1}{Q_{1}Q_{2}}\sin\phi_{1}\sin\phi_{2}\,m\omega^{2}+\] \[\cos\phi_{1}\cos\phi_{2}\,\frac{2\gamma-m\omega^{2}}{\gamma^{2}}+ \frac{1}{Q_{2}}\cos\phi_{1}\sin\phi_{2}\,\frac{\gamma-m\omega^{2}}{\gamma},\] \[m_{21} = -Q_{2}\cos\phi_{1}\sin\phi_{2}\,\frac{\gamma-m\omega^{2}}{\gamma} -\cos\phi_{1}\cos\phi_{2}\,m\omega^{2}+\] \[Q_{1}Q_{2}\sin\phi_{1}\sin\phi_{2}\,\frac{2\gamma-m\omega^{2}}{ \gamma^{2}}-Q_{1}\sin\phi_{1}\cos\phi_{2}\,\frac{\gamma-m\omega^{2}}{\gamma},\] \[m_{22} = \cos\phi_{1}\cos\phi_{2}\,\frac{\gamma-m\omega^{2}}{\gamma}- \frac{1}{Q_{1}}\sin\phi_{1}\cos\phi_{2}\,m\omega^{2}-\] (12) \[Q_{2}\cos\phi_{1}\sin\phi_{2}\,\frac{2\gamma-m\omega^{2}}{\gamma^ {2}}-\frac{Q_{2}}{Q_{1}}\sin\phi_{1}\sin\phi_{2}\frac{\gamma-m\omega^{2}}{ \gamma}.\]
###### Abstract  
Geopolymers are created by mixing a source of aluminosilicates, which can be natural or by-products of other industries, with an alkaline solution, which dissolves the aluminates and silicates present in this source, where after a polymerization process, an N-A-S-H gel is formed, which is responsible for providing the properties that characterize geopolymers. Among the variety of existing geopolymers, those based on by-products from other industries stand out since they were demonstrated to be a less-polluting alternative for concrete production than ordinary Portland cement (OPC). Due to the above, it is essential to study copper flotation tailings as raw material to generate geopolymers. The excessive amounts of existing tailing deposits also produce different risks for the nearby communities. Therefore, using this industrial waste as a construction material would provide several environmental and economic benefits. This article reports on the experimental work carried out in the laboratory of the Sustainable Mining Research Center CIMS of the Engineering Consulting Company JRI, where the effect of the alkaline activator type on the compressive strength of geopolymers based on copper flotation tailings was analyzed. For this purpose, two geopolymeric mixtures were made with different kinds of alkaline activators; one activated using 100% NaOH and the other activated with 100% sodium silicate (SS). From the results, it was found that the geopolymers activated with 100% SS obtained the highest compressive strength, reaching 36.46 MPa with 7 days of curing at 90 \({}^{\circ}\)C, followed by the geopolymers activated with 100% NaOH, where a compressive strength of 22.98 MPa was obtained under the same curing conditions. On the other hand, it was found that both geopolymers created were not leachable according to the TCLP test performed, and thus, these geopolymers were classified as non-toxic materials. In addition, it was found that both geopolymers presented a high infiltration value, making them practically impermeable.  
geopolymers; sodium hydroxide; sodium silicate; tailings +
Footnote †: journal: Applied Sciences