esa-sceva/satcom-chunk-collection huggingface dataset:
# The Production of Curli Amyloid Fibers Is Deeply Integrated into the Biology of _Escherichia coli_
## 2 Results and Discussion
### Carbohydrate Metabolism, Energy Production, and Gluconeogenesis
_oprF_ mutants in _Pseudomonas aeruginosa_ have impared anaerobic respiration and as a result, have significantly reduced anaerobic biofilm production [100]. Several terminal dehydrogenase components (_sdhA-D_) produced altered CR binding phenotypes (Table S1), where loss of the catalytic domains, _sdhAB_, lead to lower CsgA levels and the loss of stabilizing domains, _sdhCD_, lead to higher CsgA levels. Additionally, _narQ_, the primary sensor of the presence of nitrate, has been shown to alter biofilm formation and motility [101; 102], and in our study yielded decreased CsgA and CsgD levels (Table S1). Taken together, these respiration mutants point to the involvement of the electron transport chain in curli production. Previously, our lab investigated the effect of _nagA_ mutants on curli production [35]. In the Keio collection, we found _nagA_, _nagC_, and _nagK_ mutants to be defective for curli production (Table S1). For the _nagA_ strain, the decrease in curli production was similar to other K-12 strains, and less than seen in the C600 strain [35]. Intriguingly, we found _yhbJ_ and _pcnB_ strains were dark red on CR plates and produced significantly more curli than the WT strain. YhbJ has recently been shown to destabilize the RNA _glmZ_, which increases the _glmS_ transcript stability [103; 104]. _glmZ_ also regulates curli production independent of CsgD protein levels [105]. The _glmS_ transcript can be polyadenylated by PcnB and rapidly degraded [106]. GlmS transfers ammonia to fructose-6-P to form GlcN-6-P, which is later converted to UDP-GlcNAc [64]. Fructose-6-P is the product of NagA degradation of GlcNac-6-P [35], suggesting that the curli defect in a _nagA_ strain may also be due to lower amounts of fructose-6-P. The resulting lower UDP-GlcNAc levels, which are needed for lipid A, ECA, and peptidoglycan biosynthesis, may lead to a compromised cell envelope and subsequently to lower curli expression. The curli defect of the Keio _glmM_ mutant would seem to confirm this; however, _glmM_ has been reported to be an essential gene [107], and therefore its presence in the Keio collection suggests that the _glmM_ strain used in our study has acquired suppressor mutations (Tabel S1). We also found decreased curli in _nanK_ and _nanE_ (Table S1). Both are involved in sialic acid biosynthesis, which ultimately is converted to GlcNAc-6P [64] supporting the role of fructose-6-P in minimal cell envelope development for curli production.
## 3 Results
### Velocity along Cross-Section through Groin Tip and Its Influence on Recirculation Zone
Figure 11 shows the relationship between time-averaged spanwise velocity (i.e., y-velocity, \(\overline{v_{y}}\)) at points 1-3, 4-15 and 16-19 along the cross-section of Groin A and the width of recirculation zone. It was found that the time-averaged spanwise velocity on the cross-section through Groin A tip relates approximately linearly to the recirculation zone width. The linear relationship between \(\overline{v_{y}}\) at points 1-3 and recirculation zone width has a coefficient of determination \(R^{2}\) of 0.96. The \(R^{2}\) between the recirculation zone width and \(\overline{v_{y}}\) at points 4-15 reaches 0.95. The velocity distributed on the fitted line of points 1-3 were closer to each other than that of other points. In other words, the linear relationship works the best between \(\overline{v_{y}}\) at points 1-3 and the width of recirculation zone, which implies that correctly representing the spanwise velocity nearest the groin tip is the most important factor in determining the recirculation zone width. The relationship between the width and length of the recirculation zone formed behind Groin A is shown in Figure 12, with the fitting accuracy \(R^{2}\) of 0.96. It is known that the spanwise velocity at points 1-3 nearest the groin tip has a significant effect on the width of the recirculation zone (see Figure 11), which in turn affects the length of the recirculation zone (see Figure 12). In other words, the influence of Groin B on the flow structure around  
Figure 11: Relationship between the time-averaged spanwise velocity (\(\overline{v_{y}}\)) of observed points along the cross-section through Groin A and the width of the recirculation zone (\(W\)) behind Groin A.  
Figure 10: Time-averaged streamwise velocity at observed points (16–19) along the cross-section located at the Groin A tip and the length of the recirculation zone behind Groin A for each case.
## 1 Introduction  
The evolution of sedimentary facies serves as a critical archive of tectonic-climatic interactions and exerts profound controls on reservoir quality in fine-grained sedimentary systems [1; 2]. Fine-grained sediments, constituting over 70% of global basin fills [3], play a pivotal role in organic carbon sequestration, biogeochemical cycling, and hydrocarbon generation [4; 5]. The Ordovician-Silurian (O-S) transition represents a pivotal interval in Earth history, marked by the confluence of the Late Ordovician mass extinction [6], Hirnantian glaciation [7], and widespread oceanic cuxinia [8]. These events left indelible imprints on sedimentary records, particularly in the black shales of the Upper Yangtze region, which serve as a global reference for studying paleoenvironmental perturbations during this critical transition [9; 10].  
In the South China Block, the O-S transition coincided with the Guangxi Movement --a major orogenic event driven by the collision between the Yangtze and Cathaysia plates [11]. This tectonic episode triggered dramatic paleogeographic reorganization, transitioning the Sichuan Basin from a passive continental margin to a foreland basin setting [12]. The Wufeng Formation shale, deposited during this interval, preserves a complex interplay of tectonic forcing, climatic shifts, and depositional processes. Previous studies have identified diverse sedimentary facies in the Wufeng shale, including turbidites, hemipelagites, and shallow shoal deposits [13]. However, the genetic mechanisms linking these facies to syn-depositional tectonism remain poorly constrained, hindering predictive models for shale reservoir distribution.  
Recent advances in high-resolution imaging techniques, such as argon-ion polishing combined with field-emission scanning electron microscopy (FE-SEM), have revolutionized the characterization of fine-grained laminar architectures [14; 15]. Despite these technological strides, critical knowledge gaps persist regarding: (1) the hydrodynamic controls on silt-mud couplet formation in low-energy turbidites; (2) the role of volcanic ash in modulating organic matter enrichment; and (3) the temporal coupling between tectonic phases (Guangxi Movement) and vertical facies transitions.  
This study integrates core descriptions, large-section imaging, and FE-SEM analysis to address these gaps through three primary objectives:(1) Decipher the depositional processes governing fine-grained turbidite, hemipelagic, and shallow shoal facies in the Wufeng Formation; (2) Establish a tectono-stratigraphic framework linking facies evolution to stages of the Guangxi Movement; (3) Quantify the impact of depositional energy and sediment provenance on shale reservoir quality (porosity, TOC).  
The findings provide novel insights into mudstone deposition under tectonic forcing while offering practical criteria for targeting high-quality shale gas reservoirs in foreland basin settings. By bridging the gap between tectonic history and reservoir heterogeneity, this work advances predictive models for organic-rich shale distribution in syn-orogenic basins worldwide.
# FOXS-GSC--Fast Offset Xpath Service with HexagonS Communication
## 4 FOXS-GSC Performance Evaluation
### Traffic Efficiency Evaluation
#### 4.3.2 Results
In the scenario with 50 RSUs, FOXS-GSC reduces CO\({}_{2}\) emissions by more than three times (10.27%). The PTI metric, which evaluates the congestion control quality, is shown in Figure 11. FOXS-GSC with 1, 23, and 50 RSUs achieved the best PTI index results (2.68, 2.5, and 2.32, respectively) among the evaluated solutions, further confirming its excellent performance, as with 50 RSUs FOXS-GSC achieved results 19% better than the next-best solution, CHIMERA. For all metrics evaluated except distance traveled, FOX-GSC had increased performance in the scenario with larger coverage. This occurs because as coverage increases, more vehicles are able to communicate with the cloudlets, thereby receiving a more accurate route suggestions, as the cloudlets possess a global knowledge of the scenario. In the case of the distance traveled, in the scenario with 50 RSUs the cloudlets with full knowledge are always able to suggest the best and shortest route. For the scenario with 23 RSUs and the scenario with only one RSU, more vehicles need to compute the new route using only their own local knowledge. Thus, to avoid nearby congestion, FOXS-GSC sometimes takesa longer route. However, as shown, even in this case the mechanism that limits the size of the route keeps the distance traveled statistically similar to that in the PAN1, PAN3, and CHIMERA solutions. Figure 12 presents the percentages for the location where the route was computed (e.g., vehicle or cloudlet) for FOXS-GSC when varying the scenario coverage using 1, 23, and 50 RSUs. As expected, the number of RSUs covering the scenario impacts how many vehicles need to compute their own route; thus, better coverage provides better traffic management, as shown in Figures 10 and 11. Using these results, traffic engineers can make appropriate trade-offs between the cost of implementing cloudlets and the effective gain realized by improving traffic flow. Figure 11: Planning Time Index (PTI). Figure 12: Percentages of the location where the route was computed (vehicle or cloudlet).
## 2 Materials and Methods
### Shared Fiber Topology
A much better system performance can be obtained by moving the uplink at the O-band too (i.e., at 1310 nm for both downlink and uplink, where the losses are assumed to be 0.35 dB/km [52]), where the impact of SpRS gets weaker on the quantum channel operated at single-photon regime at 1550 nm. By spectrally separating the quantum and classical channel between the O-band and the C-band, the Raman contamination can be significantly limited [53]. In the first configuration, the evaluation of Raman power was performed assuming \(\rho_{dl}=8\times 10^{-10}(\text{km}\cdot\text{nm})^{-1}\) and \(\rho_{ul}=6.8\times 10^{-9}(\text{km}\cdot\text{nm})^{-1}\), while for the second \(\rho_{dl}=\rho_{ul}=8\times 10^{-10}(\text{km}\cdot\text{nm})^{-1}\)[47]. These values are typical for a single-mode fiber and correspond to the specific wavelengths that have been assigned for the classical signals. The launch power for both classical signals was set to 0 dBm, preventing the classical/quantum multiplexing scenario at C-band owing to the strong presence of Raman contamination photons [48]. The P2 MP extension of our proposed coexistence scheme is depicted in Figure 4. Exploiting the existed fiber assets for transferring both classical data flows and quantum channels becomes even more essential for P2 MP topologies where many mobile terminal nodes are interconnected through drop fibers. This shared strategy allows for significant cost-savings in ultra-dense mobile transport networks of 5G networks where the deployment costs for new fiber segments are a significant component of the total infrastructure cost [54]. The Raman noise photons behave in the same way as the Alice's encoded photons while they are transmitted from the feeder to the drop fiber segment. Therefore, even though the splitter stage puts significant pressure on the quantum layer by lowering the sifted key rate due to photon loss, the same behavior is also obtained for the noise count rates due to Raman contamination photons. Consequently, we expect that, as the number of users increases, the maximum feasible transmission distance will be getting decreased to a lesser extent compared to the P2 MP scenario utilizing dedicated fiber links for classical and quantum layers, respectively. The wavelength allocation of the classical signals is considered the same as described for the P2P case. In Section 3, SKR calculations are presented for the above topologies for both InGaAs and Si SPADs and for different key rotation times.
## 3 Results and Discussion
### Na-Dominant Clinoptilolite
#### 3.1.2 Bond Lengths and Interatomic Distances
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{3}{c}{**Optimized Siusi Na-Clinoptiollolite**} \\ \hline \hline
**T-Sites (Å)** & **Exp.** & \multicolumn{2}{c}{**rev PBE-D3**} & \multicolumn{2}{c}{**B97-D3**} \\
**(Avg. Distances)** & \multicolumn{2}{c}{**(Avg. Distances)**} & \multicolumn{2}{c}{**(Avg. Distances)**} \\ \hline Si (1)—O3 & & & & \\  —O4 & 1.61 & 1.634, 1.635, 1.623, 1.629, & 1.616, 1.617, 1.624, 1.609, \\  —O6 & & 1.633, 1.622 & 1.617, 1.624 \\  —O9 & & & \\ \hline Si (2)—O1 & & & & \\  —O10 & 1.67 & 1.622, 1.624, 1.626, & 1.616, 1.613, 1.619, \\  —O2 & 1.627, 1.627 & 1.627 & 1.622, 1.612 \\  —O4 & & & \\ \hline Si (3)—O3 & & & & \\  —O4 & 1.615 & 1.623, 1.630, 1.621, 1.615, & 1.617, 1.614, 1.622, 1.602, \\  —O7 & & 1.627, 1.621, 1.621 & 1.616, 1.622, 1.613 \\  —O9 & & & \\ \hline \hline Si (4)—O7 & & & & \\  —O10 & 1.62 & 1.624, 1.622, 1.622, & 1.614, 1.611, 1.615, 1.614, \\  —O8 & & 1.625, 1.620, 1.623, 1.627 & 1.609, 1.616, 1.613, 1.611 \\  —O10 & & & \\ \hline \hline Si (5)—O6 & & & & \\  —O8 & 1.615 & 1.628, 1.630, 1.632, 1.629 & 1.612, 1.618, 1.611, 1.619 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison between the Si-O distances in the Siusi sample of the most stable structure from DFT calculations and the experimental measurements. More information regarding these bond lengths can be found in the Supplementary Material. The underlined values are the most similar to the experimental values. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{3}{c}{**Optimized Agoura Na-Clinoptiollolite (Si-O)**} \\ \hline \hline
**T-Sites (Å)** & **Avg. Exp. ** & \multicolumn{2}{c}{**rev PBE-D3**} & \multicolumn{2}{c}{**B97-D3**} \\  & & **(Avg. Distances)** & **(Avg. Distances)** & **(Avg. Distances)** \\ \hline Si (1)—O3 & & & & \\  —O4 & 1.625 & 1.632, 1.626, 1.628, & 1.628, 1.617, 1.611, & 1.599, 1.603, \\  —O6 & 1.629, 1.629, 1.631 & 1.620, 1.616, 1.616 & 1.602, 1.615 & 1.643/1.656 \\  —O9 & & & & \\ \hline Si (2)
## 3 Experimental Results and Analysis
### Benchmarking Image Datasets  
The CASIA tampering image dataset was used in our experiments to evaluate our proposed approach. CASIA V 1.0 and CASIA V 2.0 are the two versions of this dataset, and in these experiments only the first version (V 1.0) was used in the implementation of our experiments. There are 1725 color images of 384 \(\times\) 256 pixels in the V 1.0 dataset, all of which are in the JPEG format. In this version of the benchmarking dataset, there are two groups of images: authentic and tampered. In the authentic group there are 921 images, while in the tampered group there are 800 images. The images in this datasetare categorized into eight classes based on the image contents. The images are manipulated by different kinds of copy- paste tampering operation with different sizes of forgery (i.e., small, medium, and large). Furthermore, the source of tampering could be from the same or a different image.  
In addition, the shapes of the paste- forged regions are circular, rectangular, triangular, and of an arbitrary boundary. the paste- forged regions are circular, rectangular, triangular, and of an arbitrary boundary. Figure 5, below, shows examples of the CASIA V 1.0 forged image dataset, and also the four standard images of the SIPI dataset (Lake, Lena, Mandrill, Peppers). In this figure, there are images from the animal, architecture, character and plant categories that represent the most apparent texture classes within the CASIA V 1.0 dataset. Hence, they are presented here in this figure. For the SIPI dataset, four different images are used in the experiments described in the next subsections of this research article. The images in these datasets were taken by professional people within real environments that simulate the practical applications for real-time systems.
# An Experimental Study on the Migration of Pb in the Groundwater Table Fluctuation Zone
## 2 Materials and Methods
### Experimental Methods
#### 2.3.2 Experimental Methods for Water Table Fluctuations  
The columns were filled with sand collected and processed from the field, including coarse, medium, and fine sand. The column was filled with gritstone around the 5 cm length, unpolluted medium around the 30 cm length, polluted medium around the 10 cm length, unpolluted medium around the 40 cm length, and gritstone around the 10 cm length, respectively, from bottom to top. A Pb(NO\({}_{3}\))\({}_{2}\) solution was mixed with the sample sand to produce 2000 mg\(\cdot\)kg\({}^{-1}\) contaminated mixture. After loading, the deionized water at pH 7 flowed up from the base of the column, whose pressure tube was used to measure the water table. The peristalic pump controlled the water table variation, rising or falling. The initial water table was set in the column at the beginning of the experiment at about 20 cm high. Then, the water table was adjusted to increase 10 cm per day until it had continuously risen to 60 cm by adjusting the peristalic pump. The same method was applied to the water table decrease, which decreased 10 cm per day until it reached a height of 20 cm. This process represented the completion of one water table fluctuation cycle. In the column for water table fluctuations, two cycles were continuously conducted. Figure 2 shows how the water table varies with time. In order to further understand the migration process of Pb in the medium under water table fluctuations, the Pb\({}^{2+}\) concentration adsorbed in the medium was measured by digestion treatment after the experiment. The sampling medium was obtained from the 20-60 cm height at an interval of 5 cm.
## 2 Materials and Methods
### Statistical Analyses  
The raw dataset was exported to the SIMCA-P-17.0 software package (Umetrics, Umea, Sweden) for multivariate analysis, including PCA, HCA, and PLS-DA. The PLS-DA model was validated with cross-model validation and permutations (\(n\) = 100) by describing R\({}^{2}\)Y and Q\({}^{2}\)Y values. R\({}^{2}\)X and R\({}^{2}\)Y characterize the explanatory rate of the model for x (cultivars) and y (volatile compounds) variables, respectively, and Q\({}^{2}\) reflects the predictive ability of the model. The differences between R\({}^{2}\)Y and Q\({}^{2}\) should not be larger than 0.3, which means that the established model does not overfit [19]. In addition, the Q\({}^{2}\) and R\({}^{2}\) values on the permuted datasets should be lower than the values in the actual dataset [20; 21]. The variable importance in the projection (VIP) scores was employed to rank the importance of volatile compounds in the prediction model and to point out those with the highest discrimination ability (VIP score \(>\) 1.0) between different ginseng roots. The potential discriminant compounds for the separation between groups were visualized as a heatmap using an online server, MetaboAnalyst 5.0 ([[http://www.metaboanalyst.ca](http://www.metaboanalyst.ca)]([http://www.metaboanalyst.ca](http://www.metaboanalyst.ca)), accessed on 1 December 2022), based on the following criteria: fold changes (FC) >1.5 or <0.667, and \(p\) < 0.05, Student's \(t\)-test.
## 4 Results
### Human Evaluation  
Because the target is to alter and create artistic images, there are no standard evaluation metrics. Besides the degenerative process of the proposed approach, art is tackled as the deformation reference. Therefore, traditional evaluation metrics do not apply to the presented results.  
An online form was used to gather human input for the evaluation of the presented results. The results can be found in Figure 3.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Name** & **Tested Values** & **Preferred Value** & **Description** \\ \hline \(t_{\text{\tiny{MSB}}}\) & 1 & 1 & Threshold to remove pairs with \\  & & & & lower activation values for NBB \\ \(t_{\text{\tiny{MSB}}}\) & 3–10 & 5 & Threshold to remove pairs that are \\  & & & & too close to one another for NBB \\ \(t_{\text{\tiny{P}NBB}}\) & 10–100 & 80 & Maximum number of pair points to \\  & & & & consider for NBB \\ \(\alpha\) & 1–10 & 8 & Content loss multiplier for DST \\ \(\beta\) & 1–10 & 1 & Warp loss multiplier for DST \\ \(\gamma\) & 1–10 & 5 & Regularizer multiplier for DST \\ \(lr_{DST}\) & 0.001–1 & 0.1 & DST learning rate \\ \(e_{DST}\) & 10–500 & 250 & Total number of epochs to run \\  & & & & training for DST \\ \(\beta_{2}\) & 0.8 & 0.8 & The weight applied to style content \\  & & & & for STROTSS \\ \(tx_{STROTSS}\) & 1024 & 1024 & The final texture size to consider \\  & & & & for STROTSS \\ \hline \hline \end{tabular}
\end{table}
Table 1: Different hyperparameters used in our framework.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{3}{*}{**Methods**} & \multicolumn{4}{c}{**Runtime(s)**} \\ \cline{2-5}  & \multirow{2}{*}{**Geometric Warping**} & \multicolumn{4}{c}{**Texture Rendering**} \\ \cline{3-5}  & & **256 \(\times\) 256** & **512 \(\times\) 512** & **1024 \(\times\) 1024** \\ \hline Our method & 76–119 & 65 & 111 & 183 \\ [PERSON] et al. & N/A & 13.7 & 31.1 & 117 \\ AdaIN & N/A & 0.04 & 0.14 & 0.52 \\ DST & 84–130 & 61 & 103 & 164 \\ Learning to Warp & 0.3–1.2 & 16 & 47 & 144 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison between the runtimes (in seconds) of different methods.