esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Synthesis
#### 2.2.7 Spiro-fluorene[3,4]-5-bromonapthalene (7)  
Spiro-fluorene[3,4]naphthalene (0.5 g, 1.20 mmol) was dissolved in chloroform 10 mL, and bromine (0.06 mL, 1.11 mmol) was slowly added at 0 \({}^{\circ}\)C. The solution was stirred for 3 h. By adding acetone, the mixture was quenched, and then extracted using chloroform and water. Using MgSO\({}_{4}\) for removing a small amount of water in the organic layer. Reprecipitation with methanol and chloroform was used to obtain a yellow powder: (77% Yield) \({}^{1}\)H-NMR (300 MHz, CDCl\({}_{3}\)) \(\delta\) (ppm): 9.45 (s, 1H), 8.63-8.61 (d, 1H), 8.57-8.53 (d, 1H), 8.41-8.37 (d, 1H), 8.22-8.19 (d, 1H), 7.91-7.89 (d, 2H), 7.66-7.52 (m, 3H), 7.43-7.37 (t, 2H), 7.25-7.17 (t, 1H), 7.13-7.08 (t, 2H), 6.91-6.85 (t, 2H), 6.74-6.72 (d, 2H).  
2.2.8. 2,7-Bis-[1,1',3',1'']terphenyl-spiro-fluorene[3,4]nphthalene (TP-AFF-TP)  
We put 2,7-Dibromo-spiro-fluorene[3,4]napphthalene (0.26 g, 0.45 mmol), 4,4,5,5-tetramethyl-2-[1,1',3',1'']terphenyl-5'-yl-[1,3,2]dioxaborlane (0.35 g, 0.99 mmol), tetrakis(triphenylphosphine)palladium(0) (0.05 g, 0.045 mmol) in a 3-neck round flask, and anhydrous toluene (40 mL) and 2 M sodium hydroxide 20 mL were added. The mixture was heated under reflux overnight. After the reaction was completed, the solution was subjected to extraction using chloroform and water. The product was extracted by using chloroform and water. The organic layer containing residual water was treated with MgSO4 to remove it. Column refinement under EA: hexane = 1:4 eluent was proceeded after evaporation of product in the organic layer. Chloroform and methanol were used for reprecipitation and then, by evaporating the filtered solid, 0.12 g of white power was obtained: (30% Yield) \({}^{1}\)H-NMR (300 MHz, CDCl3) \(\delta\)(ppm): 9.56 (s, 1H), 8.89-8.86 (d, 1H), 8.65 (s, 1H), 8.43-8.40 (d, 1H), 8.32-8.30 (d, 2H), 8.10-8.08 (d, 1H), 7.99-7.92 (t, 3H), 7.70-7.66 (t, 10H), 7.61-7.52 (m, 7H), 7.42-7.37 (t, 8H), 7.34-7.29 (t, 4H), 7.28-7.25 (d, 1H), 7.1 (s, 2H), 6.92-6.85 (q, 2H). \({}^{13}\)C NMR (100 MHz, CDCl3) \(\delta\)(ppm): 142.35, 139.00, 138.83, 138.44, 138.27, 135.43, 130.73, 130.26, 129.54, 128.90, 128.76, 128.58, 128.19, 128.07, 127.30, 127.20, 126.73, 126.63, 125.83, 125.40, 124.87, 124.74, 124.09, 64.89. Fab\({}^{+}\)-MS: 873 \(m/z\).
## 2 Results
### GO-Enrichment Analysis  
The organ-specific GO-enrichment analysis provided insights into the transcriptional dynamics of the significantly enriched transcripts in the leaf, stem, and root tissues of _A. glauca_. Among the biological processes, the response to gibberellin (GO:0009739), the abscisic acid-activated signaling pathway (GO:0009738), and the regulation of the cellular macromolecule biosynthetic process (GO:2000112) were recorded with significantly higher enrichments in the root tissues. However, the photosynthesis-related GO terms (GO:0009769, GO:0009768, GO:0009773, GO:0015979, GO:0009657), along with stomatal regulation (GO:0090333, GO:0010119), the cellular protein metabolic process (GO:0044267), and the organontitrogen compound biosynthetic process (GO:1901566) were significantly enriched in the leaf. Meanwhile, the stem exhibited a significantly higher enrichment of cell-wall biogenesis (GO:0042546), the cellular polysaccharide biosynthetic process (GO:0033692), and ion transport (GO:0006811). Interestingly, the secondary metabolic process (GO:0019748) showed significant enrichment in both the stem and root tissues (Figure 3A, Table S4).  
Among the molecular functions, the GO terms corresponding to the transcription-factor (GO:0003700, GO:0043565) and enzyme-inhibitor (GO:0004857) activities were enriched in the root tissue. Moreover, the hydrolase (GO:0016798, GO:0004553, GO:0016787), transporter (GO:0022857, GO:0005215), and transferase (GO:0016740) activities were significantly enriched in both the stem and root tissues. The GO terms related to binding, including pigment binding (GO:0031409), chlorophyll binding (GO:0016168), and cofactor binding (GO:0048037), were enriched in the leaf tissue (Figure 3B, Table S4).  
Figure 2: (**A**) Histogram representing significant DEGs in three combinations: leaf vs. stem (L vs. S), leaf vs. root (L vs. R), and stem vs. root (S vs. R). (**B**) Clustering of 1136 significant transcripts in leaf, stem, and root: (**a**) Subcluster 1 represents higher expressions of transcripts in the stem; (**b**) Subcluster 2 in the leaf; (**c**) Subcluster 3 in the leaf; (**d**) Subcluster 4 in the root. The color line in subfigure B represents the median values.  
Among the cellular components, the vacuolar part (GO:0044437, GO:0005774), endoplasmic reticulum (GO:0005783), cell wall (GO:0005618), and Golgi apparatus (GO:0005794) were significantly enriched in the stem and root. In concordance with the biological processes, the leaf tissue recorded significantly higher enrichments of chloroplast thylakoid related (GO:0009535, GO:0042651, GO:0009534, GO:0009570), along with photosystems (GO:0009517, GO:0009521, GO:0009522, GO:0009523), catalytic complexes (GO:1902494), and ribosomes (GO:0005840) (Figure 3C, Table S4).
# An Energy-Aware Method for Selecting Cluster Heads in Wireless Sensor Networks
## 4 Simulations and Results
Therefore, the impact on the received message increase in our proposed method in  
Figure 5: A comparison between AHP-CSP and the Base station Controlled Dynamic Clustering Protocol (BCDCP) by the number of alive nodes (system lifetime). comparison with the BCDCP method is considerable. However, it is indispensable to know, when we send data packets to the base station, what the status of the energy levels of the nodes is. As we mentioned previously, the initial energy of all nodes in the network is at most 2J. Figure 7 presents the number of transferred messages per energy unit. The number of transferred messages per energy unit to the base station in our proposed method is greater than the BCDCP method. As shown in Figure 8, the energy consumption in the proposed method (AHP-CSP) is less than the BCDCP method. In both methods, we use the cluster head routing method, and the only task of one cluster head is to send data to the base station. In the proposed algorithm, two critical factors in cluster head selection, distance to the centre and mobility, lead to the energy reduction, and in our proposed method, the energy reduction is almost 5% more than the BCDCP method. In Figure 9, the increase of the average energy consumption according to time is compared. As shown in Figure 9, the average lost energy in the BCDCP method is more than our proposed method. An appropriate cluster head selection and the data transfer load balance between nodes lead to uniform energy consumption. Figure 6: Total number of data messages received at the base station according to the number of operation rounds. Figure 7: The total number of data messages received at the base station as a function of average energy dissipation. Finally, in Figure 10, we compare the two methods in terms of the packet loss rate. As we know, data packets may be lost in two ways, insufficient capacity of the buffer in receive node and insufficient energy for packet transfer. Figure 10 shows that due to residual energy and buffer capacity, the packet loss rate in our proposed method is almost 25% lower than in the BCDCP method. Figure 8: A comparison between AHP-CSP and BCDCP in terms of average energy dissipation. Figure 10: A comparison between AHP-CSP and BCDCP in terms of the packet loss rate. Figure 9: A comparison between AHP-CSP and BCDCP in terms of average lost energy.
## 2 Background and Related Work
For comparison, the static current draw of the Xilinx Spartan-6 LX16 SRAM FPGA is 6 mA at 1.25 V, whereas that of the similar-sized Microsemi IGLOO AGL600 flash FPGA is two orders of magnitude smaller, only 30 \(\mu\)A at 1.2 V, based on datasheet reported values. Flash FPGA architectures offer reasonable processing power with favorable power saving features. We have formerly developed an application-specific WSN node for structural health monitoring [17] that employed a Microsemi (formerly Actel) IGLOO flash FPGA, relying on its instant-on startup and clock scaling capabilities. A similar flash FPGA-based node architecture has recently been investigated by [PERSON] _et al_. In [18], they compare an Altera Cyclone III SRAM FPGA and a Microsemi IGLOO flash FPGA in terms of processing performance and power consumption. They conclude, that despite the lack of hard multiplier-blocks in the flash FPGA, it still achieves reasonable energy efficiency. [PERSON] _et al_. investigated the reconfigurability of a similar, but more general, architecture in [19], also based on an IGLOO flash FPGA. Finally, [PERSON] _et al_. examined the feasibility of the 802.15.4 protocol on a flash FPGA-based architecture in [20] and found the FPGA resources sufficient, despite the lack of hardware multipliers. In this paper, we propose a modular WSN research platform for network protocol stack development and for WSN applications that demand high-speed parallel signal processing. The Marmote SDR platform leverages our previous experiences with flash FPGAs and employs a flash FPGA-based system-on-a-programmable-chip (SoPC) backed up with high-speed, low-power analog-to-digital and digital-to-analog converters (ADCs and DACs) to interface with high-bandwidth analog signals of the analog module. While our modular approach allows for dealing with signals of various nature (e.g., acoustic), the setup presented in this paper processes baseband signals of a radio front-end, hence forming an embedded SDR. The FPGA fabric resources present in the flash FPGA-based SoC are less abound, but still comparable to the logic in SRAM FPGAs in [15,16]. We traded this moderate performance for the advanced power saving benefits offered by the flash FPGA architecture.
# An Elliptical-Shaped Dual-Band UWB Notch Antenna for Wireless Applications
## 4 Discussion and Outcomes
### UWB Antenna Results
#### 4.1.3 Radiation Pattern  
Figure 12 depicts the simulation of the antenna radiation pattern for resonant frequencies within the operating band. At 3.6 GHz, a bidirectional pattern is detected in the E-plane, while an omnidirectional pattern is observed in the H-plane. However, at higher frequencies (i.e., 9.7 and 12.6 GHz) the pattern is disturbed due to higher harmonics; in addition, the cross-polarization radiation magnitudes dominate the pattern. It is evident from the figure that the cross-polarization value is much lower than the co-polarization value.  
Figure 11: Current distribution at (**a**) 3.6 GHz, (**b**) 6.8 GHz, (**c**) 9.7 GHz.
# Role of Hibernation Promoting Factor
## 3 Disscussion
In contrast, the degree of rProtein loss in _P. aeruginosa_ _Anfp_ is dependent on the physiology of the cells prior to entering starvation. The greatest loss of L5 and S13 in the _P. aeruginosa_ _Anfp_ mutant occurs when the cells are pre-cultured to stationary phase in rich medium. Polyphosphate is expected to accumulate in cells cultured to stationary phase, resulting in activation of the Lon protease [46], and greater rProtein degradation. Interestingly though, when HPF is overexpressed prior to starvation in cells pre-cultured in balanced minimal medium, the excess HPF is reduced to wild-type levels during the subsequent cell starvation. This suggests that even when the cells are not in stationary phase prior to starvation, excess HPF (possibly the HPF not associated with ribosomes) is targeted for degradation when the cells enter starvation. In _P. aeruginosa_, and in many other bacteria, the _lppf_ gene is linked to the _rpoN_ gene [41]. _rpoN_ encodes an alternative sigma factor, \(\sigma^{N}\), which is a master regulator for the nitrogen-starvation response. However, _lnpf_ also contains its own promoter (P\({}_{lnpf}\)) that is located within the _rpoN_ coding sequence [41]. The P\({}_{lnpf}\) promoter may allow cells to express high levels of _lnpf_ even when nitrogen is not limiting. Here, we tested the expression of _lnpf_ when cells were cultured to early exponential phase in balanced minimal medium, where the cells should not be limited for nitrogen, phosphate, or carbon. The results show that even in early exponential phase in nitrogen-replete conditions, _lnpf_ is expressed and detectable levels of HPF protein are produced (Figures 5 and 7). The HPF that is produced in early exponential phase is necessary for optimal recovery of _P. aeruginosa_ from subsequent starvation and for protection of ribosome integrity during starvation. Therefore, a subset of ribosomes contain HPF even in rapidly growing bacteria. Ribosome degradation is not restricted to stationary-phase cells or to cells with excess ribosomes. Ribosome degradation is also associated with quality control [44]. Ribosome assembly is a complex process, and ribosomes must pass certain quality control checkpoints prior to becoming translationally active molecules. Ribosomes that do not pass quality control are either degraded or the damaged ribosomal proteins are replaced [47]. Since _lnpf_ is expressed throughout growth and even in nitrogen-replete medium, it is possible that HPF not only plays a role in protecting ribosomes during cell starvation, but also in the ribosome assembly and quality control process. In summary, the results here show that HPF and the stringent response are required for optimal recovery of _P. aeruginosa_ from starvation. The stringent response plays a role in preventing cell lysis when the cells enter starvation. HPF is required to prevent the early steps in ribosome degradation, including loss of rRNA and loss of rProteins, which lead to insufficient ribosomes necessary for cell recovery from starvation.
# Tourism Sentiment Chain Representation Model and Construction from Tourist Reviews
## 3 Materials and Methods  
This study proposes a method for constructing a TSC based on the integration of spatial and semantic information. A Spatial-Semantic Integrated Model for Tourist Attraction Representation (SSIM-TAR) is first proposed. This model captures the comprehensive attributes of attractions through the joint representation of spatial and semantic features and facilitates the analysis of tourist multidimensional evaluations and sentiment tendencies. Subsequently, spatial, associative, and thematic-sentiment relationships among attractions are defined and quantified. A Multidimensional Association Network (MAN-SRT) is then constructed to reveal the varying strengths of inter-attraction linkages across multiple dimensions. Finally, a graph-based propagation algorithm is employed to generate an adaptive-weighted TSC, serving as a personalized travel route recommendation. The framework systematically transitions from \"attribute representation of attractions\" to \"construction of the association network\" and ultimately to \"generation of the TSC\". Theimplementation workflow is summarized in Figure 1 and will be elaborated in detail in Sections 3.1-3.3.
## 4 Implementation Details
### Evaluation Metrics  
The following metrics have been used to examine the suggested model efficiency. The confusion matrix consists of four elements: false positive (FP), false negative (FN), true positive (TP), and true negative (TN).  
Accuracy is a common metric used to evaluate classification models. The accuracy is calculated using the following Equation (4).  
\[\text{Accuracy}(\%)=\frac{\text{TP}+\text{TN}}{\text{TP}+\text{FP}+\text{TN} +\text{FN}}\times 100 \tag{4}\]  
Precision focuses on the percentage of accurately predicted positive samples (also known as true positives) among all positive predictions produced by the model. Equation (5) is used to calculate the precision.  
\[\text{Precision}=\frac{\text{TP}}{\text{TP}+\text{FP}} \tag{5}\]  
The percentage of positive samples that were correctly predicted to be positive (true positives) relative to all other positive samples is known as recall, also known as sensitivity or true positive rate. Equation (6) is utilized to learn the recall.  
\[\text{Recall}=\frac{\text{TP}}{\text{TP}+\text{FN}} \tag{6}\]  
Precision and recall are combined into a single metric called the F1 score. Equation (7) is used to determine the F1 score:  
\[\text{F1}-\text{Score}=2\times\frac{\text{Precision}\times\text{Recall}}{ \text{Precision}+\text{Recall}} \tag{7}\]
## 4 Results and Discussion
### Mathematical Modeling
#### 4.2.3 Discussion of the Two Modeling Methods  
Based on the values of R\({}^{2}\) and RMSE of the two modeling methods (Tables 5-7), the dimensional analysis method is better than the Ergun equation modifying method. This result mainly occurs because the dimensionless correlation contains six undetermined coefficients, which is more than the three undetermined coefficients in the modified Ergun equation. More undetermined coefficients can effectively improve the fitting accuracy but also increase the complexity of the fitting calculation.  
The physical laws between physical quantities are considered in the Ergun equation modifying method, so its correlation has relatively strong logicality. When using this method, by analyzing and deducing the formulas, the current fluidized state can be analyzed more deeply, and even some other inferences or conclusions can be obtained. For example, the inference of \(F_{\text{mf}}\) = 0.8995\(G\) is obtained in this study, which offers some new information on analyzing the fluidized state. The dimensional analysis only fits the relationship between physical quantities from the numerical point of view. Under the condition of the same equipment and operation parameters, it has high fitting accuracy. However, the adaptability of this method is inadequate. For example, Equations (30) and (31) have higher prediction accuracy for the negative pressure conical fluidized bed with a glass bead material, but they  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \(\mathbf{a_{2}}\) & \(\mathbf{b_{2}}\) & \(\mathbf{c_{2}}\) & \(\mathbf{d_{2}}\) & \(\mathbf{e_{2}}\) & \(\mathbf{f_{2}}\) &
\begin{tabular}{c} **RMSE** \\ **(kPa)** \\ \end{tabular} & \(\mathbf{R^{2}}\)**(-)** \\ \hline Linear regression & \(1\) & \(-0.003\) & \(-0.642\) & \(0.020\) & \(0.010\) & \(-0.354\) & \(0.015\) & \(0.994\) \\ Nonlinear regression & \(0.997\) & \(-0.003\) & \(-0.648\) & \(0.017\) & \(0.010\) & \(-0.352\) & \(0.015\) & \(0.994\) \\ \hline \hline \end{tabular}
\end{table}
Table 7: Results of the linear and nonlinear regression methods of \(\Delta P_{\text{mf}}\) for the dimensionless model.  
Figure 11: Comparison of the calculated and experimental values of (**a**) \(U_{\text{0 mf}}\) and (**b**) \(\Delta P_{\text{mf}}\) for the dimensionless model.  
may not be able to predict accurately for other types of fluidized beds or materials. These two methods have their advantages and disadvantages, and the appropriate method can be chosen according to the needs of actual production.
## 3 HTP Tooth Profile Design Using IH Tooth Profile and Cycloidal Curve
### Analysis of the HTP Tooth Profile and Conventional Tooth Profiles Through Schematic Comparison
#### 3.4.3 Analysis of Tooth Profile Meshing Geometry  
In order to compare the meshing geometries of the HTP tooth profile and the conventional IH tooth profile, simulations of actual gear meshing were performed and analyzed. Figure 9 shows the gear meshing shape of the IH tooth profile (red line) and the HTP tooth profile (blue line) under the conditions of number of teeth at z = 9, deviation coefficient at \(\lambda\) = 1.0, and eccentricity at B\({}_{\rm r}\) = 0.9. As shown in the figure, smooth contact occurs between the two gears at the meshing point, indicating that the meshing condition is very good. Furthermore, it is confirmed that no tooth interference occurs--an important consideration in gear design. The absence of interference reduces gear wear and prolongs service life, demonstrating that the HTP tooth profile offers more stable meshing compared to the conventional IH tooth profile.  
Figure 8: X-Y trajectory for each tooth curve (z = 11, r = 1, and d = 0.9).  
Figure 7: X-Y trajectory for each tooth curve (z = 11, and r = d = 1).  
As shown in the above figure, under the same conditions of \(\lambda=1.0\) and \(\mathrm{B_{r}}=0.9\), it can be confirmed that the meshing shape remains consistent regardless of changes in the \(\lambda\) value. This suggests that the Lambda value does not significantly affect the meshing geometry, and rather, the eccentricity (Br) is the main factor determining changes in the meshing shape. And it can be observed that the meshing area remains consistent and that the changes in the tooth profile due to eccentricity are not significant. This means that the HTP tooth profile has tolerance to the effects of eccentricity and can maintain a more stable meshing performance.  
Figure 10 illustrates the shape of the HTP tooth profile constructed using both the IH tooth profile and the cycloidal curve. Compared to the conventional IH tooth profile, the HTP profile more closely matches the exact curve. This demonstrates that the HTP tooth profile is designed to incorporate the beneficial characteristics of the cycloidal curve, enabling a more accurate tooth shape.  
Since the HTP tooth profile builds upon the IH tooth profile concept while incorporating the cycloidal curve, it provides visually verifiable improvements in meshing precision. As a result, the HTP profile offers reduced friction and wear compared to the conventional IH profile, extends gear lifespan, and is a more suitable structure for applications requiring high-precision power transmission.  
Figure 10: Tooth engagement shape for HTP.  
Figure 9: Shape of tooth engagement (\(\lambda=1.0\), and \(\mathrm{B_{r}}=0.9\)).
# Elemental Mercury Removal over CeO\({}_{2}\)/TiO\({}_{2}\) Catalyst Prepared by Sol-Gel Method
## 3 Results and Discussion
### The Effects of flue Gas Components on Catalysts Performance
#### 3.2.2 The Effect of HCl on Catalytic Performance  
Although the concentration of HCl is very low in the coal-fired boiler flue gas, it is still considered a decisive role in the oxidation of elemental mercury [23]. In order to study the effect of HCl concentration on the Hg\({}^{0}\) removal performance of the CeO\({}_{2}\)/TiO\({}_{2}\) catalysts prepared by sol-gel, the Hg\({}^{0}\) removal efficiencies of all catalysts were measured under the condition of 350 \({}^{\circ}\)C and 6% O\({}_{2}\). The results, shown in Figure 4b, indicate that \(\eta\) increased with the increase of HCl concentration. Low Hg\({}^{0}\) removal efficiency was found in pure TiO\({}_{2}\) catalyst. Besides, the value of \(\eta\) was also relatively low for CeO\({}_{2}\)/TiO\({}_{2}\)  
Figure 4: The effects of flue gas components on catalytic performance; (**a**) the effect of O\({}_{2}\); (**b**) the effect of HCl; (**c**) the effect of NO and NH\({}_{3}\); (**d**) the effect of the ratio of NH\({}_{3}\)/NO; (**e**) the effect of SO\({}_{2}\).  
catalysts without the existence of HCl in the flue gas. However, the elemental mercury removal performance rose sharply as the HCl was introduced, even though the concentration of HCl was only 15 \(\upmu\)L\(\cdot\)L\({}^{-1}\). That indicates that these catalysts could provide good Hg\({}^{0}\) removal performance at a low HCl concentration, which means the CeO\({}_{2}\)/TiO\({}_{2}\) catalysts prepared by sol-gel are promising for reducing the mercury emission from the boiler firing low-rank coal. The reason may be that the elemental mercury could be oxidized by CeO\({}_{2}\) in the catalysts, which has high chemical reactivity, instead of just relying solely on HCl in the simulated flue gas. Moreover, other flue gas components, such as NO, could also enhance the oxidation and removal of Hg\({}^{0}\), which will be discussed in Section 3.2.3[24]. Among all catalysts, CeO.2 Ti showed the highest Hg\({}^{0}\) removal efficiency again.