esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Test Results and Discussion  
### Enhanced Bacteria Activity  
The results of the evaluation of bacterial counts from the simple soaking method (SSM) and negative pressure method (NPM) are shown in Figure 6. In the expanded vermiculite in which immobilization was induced by NPM, the bacteria of 1.2 \(\times\) 10\({}^{9}\) cells/mL were immobilized within 3 h, whereas with SSM, 3.6 \(\times\) 10\({}^{8}\) cells/mL of bacteria were immobilized for the same period. The method of creating a negative pressure condition showed 3.3 times higher immobilization efficiency than SSM, and an approximately 2.2 times higher immobilization rate after 72 h. These results indicate that when considering the bacterial concentration (10\({}^{9}\) cells/mL) in utilizing slime-forming bacteria capable of protecting against sulfate deterioration in concrete, NPM can immobilize larger numbers of bacterial cells in a shorter amount of time, which yields enhanced resistance to the sulfate attack for repair materials.
# Carbon Balance in Salt Marsh and Mangrove Ecosystems: A Global Synthesis
## 8 Summary and Conclusions  
Mangroves and salt marshes are important storage sites for organic carbon and are among the most productive ecosystems on Earth. Mangroves store, on average, twice as much C\({}_{\text{ORG}}\) as salt marshes, although marshes have greater rates of C\({}_{\text{ORG}}\) burial. Mangroves exhibit greater rates of GPP, aboveground NPP, and canopy respiration with higher P\({}_{\text{GPP}}\)/R\({}_{\text{C}}\) ratios, whereas salt marshes exhibit greater rates of belowground root production. Mangroves have greater rates of subsurface DIC production and, unlike salt marshes, exhibit active microbial diagenesis to a soil depth of 1 m. Salt marshes exhibit greater rates of soil CH\({}_{4}\) release and greater export of dissolved CH\({}_{4}\), reflecting greater rates of subsurface methanogenesis, as competing sulfate reducers decline in activity below about the 20-50 cm soil horizon. Mangroves release greater amounts of CO\({}_{2}\) from tidal waters to the atmosphere and greater amounts of POC, DOC and DIC export to adjacent waters. Mangrove net ecosystem production (628 g C m\({}^{-2}\) a\({}^{-1}\)) is greater than in salt marshes (382 g C m\({}^{-2}\) a\({}^{-1}\)).  
Both ecosystems contribute only a small proportion of GPP and R\({}_{\text{E}}\) (ecosystem respiration) to the global coastal ocean due to their small global area, but contribute 72% of the air-sea CO\({}_{2}\) exchange of the world's estuaries, 34% of the DIC export and 17% of DOC + POC export to the world's coastal ocean. Thus, both wetland ecosystems contribute disproportionately to carbon flow in the global coastal ocean.  
This research received no external funding.  
I thank two anonymous reviewers and several other researchers for their comments on the preprint of this manuscript.  
The author declares no conflict of interest.
## 4 Verification and Validation  
### Verification of the Accuracy of the Averagely Weighted Iteration (AWI) Approach  
In this section, the volume conservation and pressure preservation are used to verify the accuracy of the AWI approach. Using identical parameters to those in the literature [22], the relaxing membrane is simulated by the explicit method and AWI method. Figure 6 shows the oscillating histories of the enclosed area and inside pressure, where lines of  
Figure 4: Flow chart of introducing the AWI approach for Cheng’s external force term.  
Figure 5: Varying history of Lagrange forces at probe points N with the explicit method and AWI approach with \(\overline{K}_{s}=13.6\). (**a**) ring history of x-lagrange forces at probe points N. (**b**) Varing history of y-lagrange forces at probe points N.  
different colors represent the results simulated by different methods. The high-frequency boundary elastic waves are shown in the area and pressure curves.  
Table 1 presents the data of enclosed area A and the pressure jump at the evolution steps, for comparison with the analytical equilibrium values. Table 1 shows that the volume leakage and pressure loss have identical magnitudes, and both are notably small. For the volume leakage, the maximum relative error obtained by the AWI method is 0.92% and is slightly better than the error obtained by the explicit method (0.99%). For the pressure jump, at \(t\) = 30,000 the maximum relative error obtained by the AWI method is 2.0% and is slightly better than the error obtained by the explicit method (3.1%), however, at \(t\) = 100,000 the maximum relative error is (1.4%) and is not better than the error obtained by the explicit method (1.2%). Overall, the results simulated by the two methods are notably close, and their relative errors compared with the analytical solution are notably small. Therefore, the AWI approach method does not reduce the accuracy of the conventional explicit IB method.
## 4 SERS Requirements
### Recommendation Model
With all this in mind, an example is provided in Table 5 of a recommendation described in terms of the recommendation model and making use of existing standards and specifications. It corresponds to the recommendation 2 used as example in Section 4.3. The above examples shows the description of a recommendation in terms of the elements and attributes defined in the recommendation model, and when appropriate, points to elements of the existing educational standards and specifications, such as the <interest>, <competency> elements in IMS-LIP, <environment> in IMS-LD, <rdceo> in IMS-RDCEO and <ScreenSize> in W3C CC/PP. Since there is no specification that deals with the interaction data, it is suggested to add an extension in the <extension> element in IMS-LIP. Moreover, the contents to be shown to the learner aimed to be readable. The link to the recommended object is built by adding the object identifier to the corresponding platform URL, for instance: [[http://www.platformhost.com/message-view?message_id=5637](http://www.platformhost.com/message-view?message_id=5637)]([http://www.platformhost.com/message-view?message_id=5637](http://www.platformhost.com/message-view?message_id=5637)). \begin{table}
\begin{tabular}{|p{142.3 pt}|p{142.3 pt}|} \hline
**Object:** forum message & **Action:** read & **Link:** 5637 (object id to be used to build the URL) \\ \hline
**Text:** “Read [PERSON]’s contributions in the ‘Module 1 forum’ of the course” \\ \hline
**Title:** “Access the contents of the message posted by [PERSON] in the Module 1 forum” \\ \hline
**Applicability conditions:** \\ \(-\) The learner has interest in Polymorphism (as defined in the <interest> element in IMS-LIP) \\ \(-\) The learner has not achieved the competences of Module 1 (as defined in the <competency> element in IMS-LIP) \\ \(-\) The learner has not read the message posted by [PERSON] (requires adding an <extension> in IMS-LIP to deal with interaction data) \\ \(-\) The screen size of the device used by the learner has to be larger than 320x200 (to be checked in the W3C CC/PP element <ScreenSize>) \\ \hline
**Restrictions:** \\ \(-\) There is a forum in the course (linked from the IMS-LD <environment> element) \\ \hline
**Category:** interest & **Stage:** doing activities & **Origin:** popular & **Relevance:** 82\% \\ \hline
**Rationale**: point to relevant information regarding the learner interest (defined in <rdceo> element in IMS-RDCEO) \\ \hline
**Explanation:** “[PERSON] has contributed to the Module 1 forum–which is related to the objective Polymorphism of the course when doing some activities related to the objective Polymorphism. According to your model, the objective Polymorphism of the course has a high interest level for you”. \\ \hline \end{tabular}
\end{table} 
Table 5: Example of a recommendation described in terms of the recommendation model.
# DCS-ST for Classification of Breast Cancer Histopathology Images with Limited Annotations
## 4 Experiment
### Limitations  
Despite the superior classification performance achieved by DCS-ST, several limitations remain. First, the model relies on a pre-trained Swin-Transformer backbone and a computationally intensive cross-scale attention module, posing challenges for deployment on resource-constrained devices. Additionally, the proposed semi-supervised learning strategy depends heavily on a large amount of high-quality unlabeled data. When such data is scarce or when pseudo-label confidence scores fail to meet the predefined threshold (\(\tau=0.9\)), performance degradation may occur. Notably, on the BreakHis dataset at higher magnifica  
Figure 8: Learning Curves of DCS-ST on BreakHis Dataset.  
Figure 9: Learning Curves of DCS-ST on Mini-DDSM Dataset and ICIAR2018 Dataset.  
tions (e.g., 400\(\times\)), the Balanced Accuracy exhibits noticeable fluctuations (\(0.8263\pm 0.0461\)), indicating room for improvement in handling diverse image distributions.  
Moreover, DCS-ST has yet to be evaluated on ultra-large-scale datasets (e.g., whole slide images) and cross-domain transfer learning tasks. Its robustness against adversarial perturbations also remains unverified. Future work will focus on model compression, enhancing domain generalization, and improving adversarial robustness further to increase the practical value of DCS-ST for clinical applications.
## 1 Introduction  
The development of built environments prioritizes the facilitation of human comfort, which encompasses thermal, acoustic, visual comfort, and indoor air quality [1]. Visual comfort specifically balances light levels to prevent discomfort from insufficient or excessive illumination. The role of daylight is pivotal in achieving visual comfort and optimizing environmental efficiency within a space [2]. The architectural discourse has shown a heightened interest in the practical and aesthetic applications of natural light. Typology is an essential methodological tool for discerning the unique characteristics and spatial geometry of the built environment [3, 4], deeply influencing daylight distribution [5]. Itnecessitates a thorough spatial analysis to understand how architectural elements such as orientation and their configurations impact the penetration and diffusion of daylight. Proper orientation not only maximizes natural light but also reduces energy consumption by minimizing the need for artificial lighting, ultimately enhancing sustainability and comfort within the built environment [6; 7], as orientation influences the intensity and angle sunlight, thus affecting the quantity and quality and distribution of daylight within the space. This analysis is critical in optimizing the use of natural light within buildings, informing design decisions that align with both functional and aesthetic goals.  
In the context of spiritual architecture, the design of lighting [8], particularly daylighting, is intricately tailored to fit the distinct spiritual practices and liturgical needs within sacred spaces like mosques and churches [9]. For mosques, this involves careful consideration of the multifunctional prayer hall, central to Islamic worship, community interaction, and religious teachings [10]. The typology of mosques ranging from the large, community-serving Masjid Al-Jami to the smaller, local Masjid Al-Hayi--is integral in defining the requisite quality of daylight that aligns with worship practices [11]. While existing studies have explored the role of daylight in sacred spaces, most have not thoroughly integrated spiritual considerations with the quantitative and qualitative analysis of daylight. For instance, research has compared the spatial and daylighting dynamics in Byzantine churches and Ottoman Mosques [12], while others have focused on the illuminative effects of specific architectural elements like mosque domes [13]. However, these findings are often not generalizable across different typologies. This study aims to investigate deeper into the typological analysis to assess visual comfort within mosques, exploring how sensory and contextual factors affect daylight distribution and its contribution to spiritual ambiance. It addresses the gap in understanding how various mosque daylighting typologies influence visual comfort, analyzing daylight performance in diverse mosque designs to evaluate the visual comfort levels of worshippers.
# Pet Fiber Reinforced Wet-Mix Shotcrete with Walnut Shell as Replaced Aggregate
## 3 Experimental Analysis
### Relevant Discussion
#### 3.5.2 Relationship: Rebound-Density and Rebound-Build-Up Thickness  
The rebound rate was correlated with the density of mixture in Figure 18. The rebound rate increased with an increase in density, excepting the outlier that is mixture W3 in Table 1. As the normal trend, a similar linear relationship was plotted in Equation (6) with a good coefficient of determination, \(R^{2}\) of 0.8954. The result was similar to an earlier study on the influence of particle density on rebound that revealed that a lower density was beneficial for reducing rebound [(60; 72)].  
[PERSON] and [PERSON] [(75)] illustrated the tendency for a particle to rebound was related to a tradeoff between its ability to indent the substrate and its susceptibility to ejection. A lightweight aggregate was more likely to resist ejection when reaching the wall sprayed [(72)].  
Therefore, some crushed walnut shell as light aggregate may not even be able to reach the substrate but rather be fanned out from the stream. This was why outlier appeared in W3, owning the highest rebound and smallest density, with higher percentage of walnut shell than other mixtures. The reason also explained the larger divergent angle of spraying W3 in Figure 10.  
\[y=0.016x-18.588 \tag{6}\]  
where \(y\) is rebound rate (%) and \(x\) is density (kg/m\({}^{3}\)).  
A linear relationship between the rebound rate and build-up thickness is also shown in Figure 19 with Equation (7), excepting outlier of W3.  
\[y=-0.1145x+25.731 \tag{7}\]  
where \(y\) is rebound rate (%) and \(x\) is build-up thickness (mm).  
Figure 17: The relationship between compressive and splitting tensile strength of shotcrete without aggregate replacement.  
Figure 18: Relation of rebound and density.  
As reported in the literature [76], the build-up thickness presented an inverse correlation with rebound rate. It also can be seen that adding fibers is beneficial to shootability of fresh concrete, along with low rebound and high build-up thickness. Figure 19 shows that the build-up thickness increased from 90 to 140 mm when the rebound rate declined by about 30%. These results indicated that crushed walnut shell could be used to replace partial natural coarse gravel for attempting to produce lightweight shotcrete with acceptable mechanical characteristic, pumpability and shootability. Furthermore, PET fiber made from waste plastic bottles was mixed into fresh concrete to enhance its splitting tensile strength, also improving the pumpability and shootability of concrete mixture, but slightly reduced its compressive strength. According to Chinese standard [44], PET fiber wet-mix shotcrete with crushed walnut shell of about 35% coarse aggregate replacement could be used for roadway support as lightweight shotcrete.
## 3 Experiment and Results Discussion
### Effect of the Influence of Environmental Factors on UV-Vis Spectroscopy
#### 3.1.1 Effect of the Influence of pH on UV-Vis Spectroscopy
(**a**) The influence of pH on the UV-Vis spectra of the standard solution. (**b**) The change in absorbance at different wavelengths with the pH of the standard solution. molecules. Consequently, this leads to changes in the spectral absorption band, with the absorption band broadening and the linear range decreasing as the pH becomes increasingly alkaline, ultimately affecting the absorbance. As shown in Figure 4b, the absorbance of the water sample has a spectral trend similar to that of the standard solution (as shown in Figure 3b). From the above analysis, it is evident that the pH level has a minimal or unclear influence on UV-Vis spectroscopy when the pH ranges from one to four. However, when the pH value is above four, the overall trend of the UV-Vis spectrum will significantly shift downwards, accompanied by red and blue shifts. This indicates that the influence of pH on UV-Vis spectroscopy is not consistent across the entire pH range. Consequently, significant changes in the pH of a sample can introduce substantial errors in COD detection using UV-Vis spectroscopy. Given the wide range of pH variations in common surface waters, such as domestic sewage, rivers, and lakes, it is crucial to account for and compensate for the influence of pH to enhance the accuracy of COD detection.
# Generation of Monoclonal Antibodies for Sensitive Detection of Pro-Inflammatory Protein S100A9
## 3 Results
### Generating a Highly Sensitive Anti-hS100A9 Monoclonal Antibody  
We used hybridoma technology to generate a monoclonal antibody by fusing an antibody-producing B cell from immunized mice with an immortal myeloma cell. First, we produced antigen-specific B cells by immunizing BALB/c mice. The mice were injected with rhS100A9 at 14-day intervals. The isolated B cells were fused with the myeloma cells to generate hybridoma cells capable of producing virtually unlimited quantities of monoclonal antibodies. The hybridoma supernatants were collected, and the anti-hS100A9 antibodies in the supernatants were screened to confirm their capacity to bind hS100A9. We selected 24 supernatants that showed positive signals with a high ELISA titer, then selected five colonies that showed a high titer (colony numbers 5, 7, 9, 14, and 22), and cultured each of them for a second screening. From the cultured cells, 96 colonies were picked and transferred to 96-well plates. After ELISA, we selected one clone from each plate that showed a high titer relative to the colony's size; that is, having a high possibility of being a single clone. We spread the cultured cells from each selected parent clone, separated each colony, and selected the best one from each plate through indirect ELISA (5B3, 7C8, 9D9, 14C4, and 22A10). After a third screening, we extracted antibodies from the five selected hybridoma cell supernatants and purified them using Protein G agarose resin. Next, we checked the activity of the antibodies through an indirect ELISA using various concentrations of commercial hS100A9 as an antigen to confirm that all of the antibodies had hS100A9 binding affinity (Figure 2 and Table 1).
# HybridTabNet: Towards Better Table Detection in Scanned Document Images
## 7 Conclusions and Future Work  
This paper presents a novel approach, HybridTabNet, for table detection from scanned document images. The approach uses the ResNeXt-101 backbone for feature extraction and also replaces regular convolutions with deformable convolutions. The proposed approach is the Hybrid Task Cascade network for table detection that uses cascade architecture, for instance, segmentation. Our method surpassed the existing state-of-the-art table detection methods in all the datasets except for ICDAR-2017-POD. The relative improvement of error in terms of the weighted average amounted to 27.57% for ICDAR-2019-TrackA-Modern, 42.64% for TableBank (Latex), 41.33% for TableBank (Word), 55.73% for TableBank (Latex + Word), 10% for Marmot, and 9.67% for UNLV. For ICDAR-2013, the proposed approach achieved a perfect score for precision and recall, which is on par with the previous state-of-the-art method. However, for ICDAR-2017-POD, the proposed approach did not outperform the state-of-the-art methods. This is because ICDAR-2017-POD contains a lot of other graphical page components that are similar to tables. Other methods rely on pre-and/or post-processing to transform the data for favorable results. However, our approach works on raw images. Moreover, we incorporated the leave-one-out evaluation for all the datasets, which demonstrated the algorithm's generalization capabilities--a direction for evaluating table detection algorithms to follow in the future.
# Application of Mathematical Models for Blood Flow in Aorta and Right Coronary Artery
## 2 Materials and Methods
### Boundary Conditions  
We defined the boundary conditions that, together with the initial conditions set to define the starting state of the fluid (blood) in the computational domain, were fundamental to ensure that the simulation was well posed and could proceed with the iterative process for solving the system of equations until convergence was reached. A no-slip condition was imposed on the walls, meaning zero velocity at these surfaces [14; 15]. The rigid wall assumption causes small errors in the estimation of the fluid dynamic parameters. However, this should not influence our results, because the general flow characteristics remain similar [16; 17]. Furthermore, the level of uncertainty in the patient-specific domain for vascular tissue, as well as the significant increase in computational costs of fluid-structure interaction (FSI) simulations, make this approximation currently the best compromise between accuracy and efficiency for clinical purposes [18; 19; 20]. In some cases, the stiffness of the vessels is increased so significantly that the hypothesis of a rigid wall may be valid. For steady-state simulations, the maximum value of the pulsatile flow rate (corresponding to the systolic peak) was applied at the inlet, while pressure was specified at the outlet. Specifically:  
* Aorta: At \(t=0.13\,\mathrm{s}\), the inlet flow rate is \(Q_{\mathrm{in}}=450\,\mathrm{mL/s}=450\times 10^{-6}\,\mathrm{m}^{3}/ \mathrm{s}\)[21], and the outlet pressure is \(P_{\mathrm{out}}=70\,\mathrm{mmHg}=9332.54\,\mathrm{Pa}\)[22].
* RCA: At \(t=0.2\,\mathrm{s}\), the inlet flow rate is \(Q_{\mathrm{in}}=2.25\,\mathrm{mL/s}=2.25\times 10^{-6}\,\mathrm{m}^{3}/ \mathrm{s}\)[23], and the outlet pressure is \(P_{\mathrm{out}}=133\,\mathrm{mmHg}=17,731.83\,\mathrm{Pa}\)[24].
# The Recent Advancement in Unmanned Aerial Vehicle Tracking Antenna: A Review
## 4 Recent Advancement on UAV Tracking Antenna
### UAV Tracking Techniques
#### 4.3.3 UAV Tracking Based on GPS  
The significant errors that accompany the previous schemes limits their versatility and, hence, the need for global positioning system (GPS) module. A GPS tracking system is a very common approach in getting vehicles location information in real-time for fleet planning [106]. This mode of tracking uses the unique advantages of the GPS to get the location of the UAV and that of the tracking system. The coordinates (latitude and longitude) of the UAV and tracking antenna are involved, and the various tracking parameters can be estimated. Using models, such as haversine distance and bearing formula, spherical trigonometry, calculation for the azimuth angle, elevation angle, and distance of the UAV from the tracking system, can be calculated. Their advantages resonate around its high precision and accuracy [31; 89; 107; 108]. Many others built an antenna tracker using GPS technology. They were all able to track the UAV and improve the range of communication between the GCS and the UAV, as a result. The accuracy of this approach suffers significantly in the loss of GPS signal as well as when tracking system moves from outdoor to indoor environment where there is no clear LoS. Some researchers have proposed the use of anchor nodes equipped with passive radars to track UAV on the loss of GPS signals [109; 110]. To address the issue of GPS positioning error, Allan variance (AVAR) has been proposed [111]. Notwithstanding, navigation errors can arise from poor satellite visibility or less than optimum geometry from the satellites that are visible. Accuracy of ephemeris data (i.e., each satellite's positional information) is fundamental to the accuracy of the system. There are external effects that will affect the GPS signal which introduces errors and, subsequently, affect accuracy [54]. Multipath ranging errors can be caused by reflections of the GPS signals from mountains and tall buildings. In the event of poor satellite coverage for defined periods (typically less than 30 s), the system uses other navigation sensor inputs to enter into a dead reckoning mode. For prolonged periods of poor satellite reception, the system must reenter the acquisition mode.