esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Results and Discussion
### Reusability of the Polymer-Supported Asymmetric Catalytic System  
Finally, we investigated the reusability of the polymer-supported NHC-Ir catalyst system in asymmetric silane reductions. For this purpose, we selected two ketones, **7** and **17** (Table 4). In this study, we used commercially available Merrifield resin (200-400 mesh) with particle sizes ranging from 38 to 75 \(\upmu\)m as the starting material for the synthesis of PS-**L3**. A membrane filter with a pore size of 0.2 \(\upmu\)m was used for the reusability test. After performing the asymmetric silane reduction reactions of ketones **7** and **17** using the [IrCl(cod)]\({}_{2}\)/PS-**L3** catalytic system, solid-state resins **X** and **Y** were recovered in 49 and 52 mg yields, respectively, through simple filtration using a membrane filter (entry 1, run 1 and entry 2, run 1).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \({}^{cot}\) & \([\mu\)Cl(cod)]\({}_{2}\) & Ag\({}_{2}\)CO\({}_{3}\) & (4.5 equiv. wt 1) \\ /PS-**L1** & THF & THF \\ (6 mol/\% - Ir cat) & \(r.t.,3\) h & \(r.t.,20\) h \\ \hline \hline
**Entry 1** & Substrate & Product & Yield [\%] 2 [FOOTNOTE:2]Footnote 2: The catalytic reaction was performed as follows: A mixture of [IrCl(cod)]\({}_{2}\) (0.015 mmol, 10 mg), PS-**L3** (0.03 mmol, 42 mg), and Ag\({}_{2}\)CO\({}_{3}\) (0.015 mmol, 4 mg) within THF (2 mL) was stirred for 3 h. Then, (EO)\({}_{2}\)MeSHI (2.25 mmol, 302 mg) was added, and the mixture was stirred for 20 h. Subsequently, ketone (0.5 mmol), \({}_{2}\)CO\({}_{3}\) (5 mg), and MeOH (2 mL) were added, and the reduction reaction was allowed to proceed for 6 h. 2 Isolated yield. 3 Ee was determined by GC using a chiral stationary phase. 4 Yield was determined by GC using the internal standard method. 5 Ee was determined by LC using a chiral stationary phase. & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Ir-catalyzed symmetric silane reduction of various ketones under heterogeneous conditions.  
The result showed that the combination of [IrCl(cod)]\({}_{2}\) and **X** successfully catalyzed the reduction of ketone **7** using the _pre-mixing_ reaction procedure, yielding (S)-**8** with 77% ee (entry 1, run 2). Similarly, ketone **17** was reduced to (S)-**18** in good yield with 61% ee when catalyzed by [IrCl(cod)]\({}_{2}\) combined with **Y** (entry 2, run 2). In contrast, no reaction occurred when [IrCl(cod)]\({}_{2}\) was absent (entry 1, run 2A). These results also suggest that the silane reduction is not promoted by a silver base alone. Although the exact reaction mechanism involved in this reusability test remains unclear, it is possible that an inert Ir(III) species in **X** (or **Y**) is replaced by the Ir(I) species in [IrCl(cod)]\({}_{2}\), thereby regenerating the polymer-supported NHC-Ir(I) active catalyst. Further investigations into the reusability of this catalytic system are currently underway.
# Text Indexing for Faster Gapped Pattern Matching
## 3 An Improved Solution
### Correctness  
The key observation from Figure 2 is that the union of the leaf nodes in the tree constructed by Algorithm 1 resembles the blocking scheme described in Section 2 (see Figure 1 for comparison). Based on this observation, we now formalize the following key lemmas.  
**Lemma 3**.: _Every subarray \(A^{\prime}\) of size at most \(G\) is contained in the subarray of some leaf._  
Proof.: For the sake of contradiction, assume that subarray \(A^{\prime}\) is not contained in the subarray of any leaf. Let \(v\) be the node of maximum height that contains \(A^{\prime}\). Let \(l\) and \(r\) denote the bounds for the subarray for node \(v\), and let \(m=\lfloor(l+r)/2\rfloor\).  
Since \(A^{\prime}\) is not contained in any leaf, it must be that \(A^{\prime}\) is not fully contained within the range \([m-G+1..m+G]\). Therefore, \(A^{\prime}\) either starts in the range \([l..m-G]\) or ends in the range \([m+G+1..r]\). In the former case, since \(m-G+|A^{\prime}|-1\leq m-G+G=m\), we have that \(A^{\prime}\) must be contained within the subarray of \(v.\)left_child, which contradicts the assumption that \(v\) is the highest node containing \(A^{\prime}\). In the latter case, since \(m+G+1-|A^{\prime}|+1\geq m+G+1-G+1>m+1\), we have that \(A^{\prime}\) must be contained within the subarray of \(v.\)right_child, which again contradicts the assumption that \(v\) is the highest node containing \(A^{\prime}\).  
Figure 2: Tree structure constructed by Algorithm 1 with \(N=32\) and \(G=4\).  
The correctness of the above query procedure then follows from the fact that every block of size at most \(G\) is contained within the subarray of some leaf node \(v\). All ancestors \(u\) of leaf \(v\) will report that they contain an occurrence, allowing the DFS traversal to continue until leaf \(v\) is reached and its occurrences are reported.
## 3 Results and Discussion
### Disturbed by Turbidity
#### 3.2.2 Establish Turbidity Compensation Model  
Aiming to establish the turbidity compensation model, we carried out the following experiments. We added the turbidity solutions into the COD solutions to produce ten different mixtures, as shown in Table 8. The UV-Vis absorption spectrum of these mixtures and COD solution whose concentration was 20 mg/L are shown in Figure 14. Their first derivative spectra are shown in Figure 15.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Processing Methods** & \(R^{2}\) & **Average Relative Error** & **Maximum Relative Error** \\ \hline Single-wavelength & 0.98652 & 7.18\% & 22.02\% \\ \hline Double-wavelength & 0.98787 & 5.52\% & 12.99\% \\ \hline \hline \end{tabular}
\end{table}
Table 7: Prediction results of double-wavelength method and single-wavelength method.  
Figure 14: Original UV–Vis absorption spectra of solutions with different turbidity.  
In order to establish a turbidity compensation model for getting \(\tau_{i}(i=1,2,3,4,5)\), we had to calculate \(\beta_{i}(i=1,2,3,4,5)\) first. The fitting results between \(\beta_{i}(i=1,2,3,4,5)\) and turbidity values are shown in Figure 16. The turbidity values can be calculated by DWM.  
It is clear that \(\beta_{1}\) caused by turbidity can be represented by two linear relationships, while the variables \(\beta_{2}\), \(\beta_{3}\), \(\beta_{4}\), and \(\beta_{5}\) can be represented by one linear relationship. A series of mathematical equations were developed which describes the relationship between turbidity values and variables. The equations are shown as follows:  
\[\beta_{1}=\left\{\begin{array}{l}-0.0022x-0.0155(0<x<25)\\ -0.0008x-0.0499(25<x<50)\end{array}\right. \tag{19}\]  
\[\left\{\begin{array}{l}\beta_{2}=-0.0002x-0.0053\\ \beta_{3}=-0.0001x+0.0005\\ \beta_{4}=-0.0001x-0.0011\\ \beta_{5}=-0.0001x-0.0005\end{array}\right.\left(0<x<50\right) \tag{20}\]  
where \(x\) is the turbidity value. The correlation coefficients of these linear equations are shown in Table 9.  
Figure 16: The linear relationship between the turbidity values and \(\beta_{i}(i=1,2,3,4,5)\).  
Figure 15: The first derivative of the UV–Vis absorption spectra of solutions with different turbidity.  
It can be seen from Table 9 that \(\beta_{i}(i=1,2,3,4,5)\) and turbidity values have good linear relationships and \(\beta_{i}(i=1,2,3,4,5)\) can be calculated by Equation(19) and Equation(20). From Equation (15), the turbidity compensation of the first derivative spectrum can be completed and \(\tau_{i}(i=1,2,3,4,5)\) can be obtained. \(\tau_{i}(i=1,2,3,4,5)\) are the characteristic values, which can be used to detect COD values with turbidity interference.
# Comparative Study of Energy Efficient Routing Techniques in Wireless Sensor Networks
## 3 The Proposed Taxonomy
### Delivery Mode  
In some applications, the sensed data can be sent far from any temporal constraint, and data always remains useful, unlike other applications that need even more exactness and require real-time communication. From here, we classified routing protocols into real-time protocols and non-real-time protocols. We detail these two types of message delivery requirements in the following.
## 3 Simulation Setup and Performance Evaluation
### Bandwidth Consumption  
In this experiment, we want to send a file of 200 MB size. Our goal is to determine the bandwidth consumption for different real-time QoS routing protocols. Based on the simulation, we observe that all protocols have similar bandwidth consumption trends except PT which consumes less bandwidth for sending a file of 200 MB size within same amount of time. The reason for the lesser bandwidth consumption is the routing of the flow of packets through numerous different paths to the base stations. In addition, PT uses the multipath fairness solution that reallocates the network bandwidth from higher data sources to the lower data sources as long as the sensor nodes use common routing paths. Figure 10 shows the bandwidth consumption for the real-time QoS routing protocols that range from 220 Kb/s to 266 Kb/s. However, PT consumed bandwidth from 186 Kb/s to 208 Kb/s during the file-sending process. The simulation results validate that higher bandwidth consumption can affect the throughput.
## 2 Materials and Methods
### Surface Roughness Measurements  
Surface roughness (Ra) was assessed using a profilometer (Perthometer M2, Mahr, Gottingen, Germany) at baseline (Ra1) and after the immersion time (Ra2). In order to measure the roughness, a diamond stylus with a 5-\(\upmu\)m tip radius was used. The stylus was moved across the ceramic disc surface under a constant load of 3.9 nM at a speed of 0.12 mm/s. The readings made before and after simulated acid exposure (18 h) were carried out on the same location and the procedure was repeated 4 times for each specimen.
# A Trusted Routing Scheme Using Blockchain and Reinforcement Learning for Wireless Sensor Networks
## 4 Experimental Analysis and Evaluation
### Efficiency  
In the efficiency experiment, for a more intuitive comparison, we compared our blockchain system based on the PoA consensus mechanism with the traditional PoW-based blockchain system. We recorded the experimental data such as transaction latency, energy consumption and throughput during the experiment.
## 3 Laboratory Tests
### Temperature Effect  
To investigate the effect of temperature on the dielectric constants obtained from the PTDR, the PTDR module was submerged in distilled water in a plastic cylindrical tank with a 500 mm diameter. The temperature of distilled water was changed, ranging from 0.1 to 15.2 \({}^{\circ}\)C, based on the annual subsurface temperature of the study site reported by [PERSON] et al. [36]. Figure 6 shows that the second reflection of the waveform in water obtained from the PTDR became more rounded when compared to the typical waveforms obtained from conventional TDR probe. Considering the PTDR was connected to the short cable with 3 m length, the rounded waveform may not be affected by the cable length, but rather, can be affected by the physical construction of the PTDR module. The travel time between the first and second reflection in the water remained constant throughout the change of temperature. This means that temperature had a minor effect on the travel time determined in the time domain. It should be noted that this result was obtained by using PTDR, not a conventional TDR probe. [PERSON] and [PERSON] [44] showed that the dielectric constants of loamy sand changed with the change in temperature, but the dielectric constants almost remained constant in the range of temperature from 0 to 10 \({}^{\circ}\)C. According to the results by [PERSON] and [PERSON] [44], the variation in the dielectric constants of loamy sand was influenced more by water content than by temperature. Considering the temperature range measured at the site (0 to 6 \({}^{\circ}\)C), the effect of temperature in this study was not taken into account for the estimation of dielectric constant by the PTDR. The minor influence of temperature on the dielectric constant measurement makes the PTDR more accurate in the estimation of water content in the active layer.  
Figure 5: Calibration using soils sampled at Ny-Alesund, Svalbard: (**a**) TDR waveforms; (**b**) correlation between the dielectric constant and volumetric water content.  
Figure 6: Effect of temperature on the dielectric constant estimated from using PTDR in distilled water. ’T’ denotes the temperature of water.
## 5 Human-Technology Comparison
### Performance Comparison
#### 5.1.3 Visual-Tactile Inspection  
In the last comparison, the operators' performance was compared to a 3D scanner. While the human was able to detect 84.0% of defects, the scanner was able to perceive any deviations in shape (even very small ones), thus outperforming the human, U = 1703.0, \(p\) < 0.05. This outstanding performance was at the expense of inspection time. The 3D  
Figure 7: Mean plot of inspection accuracy in the borescope inspection by defect type and for different inspection agents.  
scanner took approximately 55 s for a full scan of the blade. In contrast, the human operators, with 22.140 s, were twice as fast and thus more efficient, \(Z=-1.581\), \(p<0.05\).  
The mean plot of achieved detection rates in Figure 8 shows that the greatest potential for 3D scanning lies in the inspection of bends, dents and nicks. Moreover, the incorrect removal of serviceable blades from service could be eliminated by 3D scanning the parts. The scanning and processing time of the data took approximately 55 s--twice as long as the operator--and did not include the setup time. Thus, while scanning each blade might lead to optimum inspection accuracy and quality, it is a time-consuming task that could create a bottle neck in the operation. Thus, both measures should be taken into account when considering 3D scanning for blade inspection.
# TrendFlow: A Machine Learning Framework for Research Trend Analysis
## 2 Related Work
### Dimension Reduction  
To reduce computational costs, we aim to employ dimensionality reduction techniques on word embeddings. The primary objective of dimensionality reduction methods is to transform high-dimensional datasets into lower-dimensional representations while retaining most of the information. This trade-off involves finding the right balance between the number of dimensions and information preservation. Although reducing dimensions may lead to a slight loss of information, it helps mitigate the challenges associated with high dimensionality and simplifies tasks such as visualization, training, and clustering on lower-dimensional datasets. Some popular dimensionality reduction methods include principal component analysis (PCA) and uniform manifold approximation and projection (UMAP).  
**PCA:** PCA [12] is a widely-used dimensionality reduction technique. It extracts the main feature components (principal components) of the original data by solving an eigen decomposition problem. PCA then changes the basis of high-dimensional data and generates a final low-dimensional representation. The technique assumes normalized input features, making it a general-purpose method applicable to any dataset.  
**UMAP:** UMAP [13] is a dimensionality reduction technique based on Riemannian geometry, algebraic topology, and graph layout algorithms. Unlike PCA, UMAP relies on several assumptions about the data. Firstly, it assumes that the data is uniformly distributed on a Riemannian manifold. Secondly, the Riemannian metric is considered to be locally constant. Lastly, the manifold is assumed to be locally connected. The algorithm operates by constructing a high-dimensional graph that represents the original data. Subsequently, it identifies a low-dimensional graph that most closely resembles the high-dimensional counterpart through iterative optimization.
## 3 Results
### Geopolymer Characterization
#### 3.2.2 Mineralogical Analysis  
Powder X-ray diffractometry analyses were performed in order to estimate the modification or formation of crystalline phases eventually induced by the geopolymerization phenomenon, as a consequence of the alkaline activation of metakaolin, biochar and IBA.  
XRD patterns of the first set of samples (Metakaolin + Biochar) (Figure 6a) show no changes with the curing time. The patterns were characterized by two crystalline phases: at 20 = 26.6\({}^{\circ}\), quartz (SiO\({}_{2}\)), which derives from metakaolin and at 20 = 29.4\({}^{\circ}\), calcite (CaCO\({}_{3}\)), which derives from biochar. Indeed, calcite was evident only in the sample containing 70% of biochar. All the samples maintain the broad band typical of amorphous structures between 20-40\({}^{\circ}\) 20 and a shift was observed with respect to the broad band characteristics of metakaolin, confirming geopolymerization. Samples of the third composition showed no mineralogical changes. The presence of quartz and calcite were observed because biochar was substituted for only 50% of the metakaolin.  
XRD patterns of the second series samples (Metakaolin (MK) + IBA + Biochar) (Figure 6b) showed three principal peaks: at 20 = 26.6\({}^{\circ}\), quartz (SiO\({}_{2}\)), due to metakaolin, at 20 = 29.4\({}^{\circ}\), calcite (CaCO\({}_{3}\)), due to the biochar content, and at 20 = 31\({}^{\circ}\), gehlenite (Ca\({}_{2}\)Al(AlSi)O\({}_{7}\)), due to the IBA content. In general, the patterns of geopolymers presented an amorphous band at about 20 = 25-35\({}^{\circ}\), similar to other amorphous materials such as silicate glass. On the contrary, the broad band characteristic of amorphous carbon in biochar is located between 20 = 15-25\({}^{\circ}\) and it is not visible in geopolymers. The patterns of the geopolymers (first and second series) showed no change as time of curing increased.  
Figure 6: XRD patterns of (**A**) the first series: (a) MK100%, (b) MK + 20%Biochar and (c) MK + 70%Biochar and (**B**) the second series: (d) 50%MK/50%IBA, (e) 50%MK/50%IBA + 20%Biochar and (f) 50%MK/50%IBA + 70%Biochar (C = calcite; Q = quartz; G = gehlenite) at 90 days of curing.
## 6 Strengths, Limitations, and Future Research Directions
### Future Research Directions
#### 6.3.3 Performance Optimization  
Deep learning architectures require substantial amounts of labelled data to achieve high performance. However, labelling data is time-consuming and expensive. Future research should explore ways to improve the efficiency of deep learning models, such as developing semi-supervised learning methods or transfer learning (following up on the finding in [10]) that can leverage pre-trained models.  
In addition, the necessity of large amounts of labelled data requires significant computational resources, which limits the deployment on resource-constrained devices. Future research should focus on developing architectures that are optimized for deployment on these devices, as well as exploring ways to reduce the computational cost of existing architectures. It should explore the advantages of the implementation of the knowledge distillation of deep learning architectures to reduce computational resources.
## 3 Results and Discussion
### Effect of Allicin on Mutants Impaired in Auxin Homeostasis and Signaling  
Because we observed an effect on auxin distribution and PIN localization after allicin treatment (Figure 5), we investigated the effects of allicin on the auxin transport mutant aux1-2 and the auxin-resistant mutant axt1-12 in comparison to wild type Col-0. Whereas the PIN proteins are functional in auxin efflux at the basal cell pole, the AUX1 protein transports auxin into the cell from the apoplast at the apical pole.  
The Col-0 wild type showed that its exposure to 0.5 mM of allicin led to a significantly shortened root. The aux1-2 mutant root length, which already had a significantly shortened root in the control compared to the Col-0, was not further affected by the presence of allicin, i.e., this mutant showed a degree of allicin resistance (Figure 6A) and this suggests that the root phenotype we observed with allicin may have been dependent upon auxin over-accumulating to supraoptimal levels and that inhibited root growth. The axr1-12 mutant, on the other hand, showed no significant change in root length phenotype compared to the wild type in the control, but treatment with allicin caused the roots to be shortened, but to a much lesser extent than in the Col-0 control. This suggests that the influence of allicin on auxin transport was partly responsible for the observation that the roots did not grow as well under allicin exposure (Figure 6B). Possibly, another auxin receptor could have partially complemented the phenotype of axr1-12. To test this hypothesis, Col-0 and axr1-12 mutants were exposed to 20 \(\upmu\)M of cytochalasin, which led to disruption of the actin cytoskeleton. As a control, it was examined whether the solvent DMSO at the concentration used (1% \(v/v\)) would lead to an effect, which was not the case. While the Col-0 wild type showed significantly shortened roots after treatment with cytochalasin, the axr1-12 mutant did not, which suggested that it was disruption of the actin cytoskeleton which altered auxin localization. Accordingly, a mutant that was not an auxin receptor mutant showed no phenotype in terms of root length upon destruction of the actin cytoskeleton. These results suggest that the effect allicin has on root development correlates, at least in part, with its influence on auxin distribution in the root, which are dependent upon allicin's effects on the cytoskeleton.