esa-sceva/satcom-chunk-collection huggingface dataset:
# Study on the Properties of Cement-Based Cementitious Materials Modified by Nano-CaCO\({}_{3}\)
## 4 Results and Discussion
### Effect of Nano-CaCO\({}_{3}\) on the Hydration Heat of Cement  
The influence of various amounts of Nano-CaCO\({}_{3}\) on the heat of hydration is presented in Figure 8. In the early stage of the hydration, the exothermic rate of cement with a Nano-CaCO\({}_{3}\) content of 2.0% was the highest, and in the process of the Nano-CaCO\({}_{3}\) content increasing from 0 to 2.0%, the rate of the hydration exothermic heat increased gradually with the increase of Nano-CaCO\({}_{3}\) content. An explanation can be that the high specific  
Figure 6: Flexural strength of mortar with different amounts of Nano-CaCO\({}_{3}\).  
Figure 7: Average permeability height of mortar with different amounts of Nano-CaCO\({}_{3}\).  
surface area of Nano-CaCO\({}_{3}\) caused a nucleation effect, promoting cement hydration and releasing a lot of heat.  
In the later stage of hydration, C-S-H gel and Ca(OH)\({}_{2}\) were coated around the clinker particles, which hindered the reaction of water and clinker particles and delayed the hydration. At the same time, the hydration heat in specimens after 3 d with 0%, 0.5%, 1.0%, 2.0%, 3.0% and 4.0% Nano-CaCO\({}_{3}\) accounted for 27.9%, 30.7%, 32.9%, 42.4%, 42.1% and 31.8% of the total hydration heat after 28 d, respectively. It was further proven that nanoparticles could accelerate the early hydration of cement, and the heat release rate is positively correlated with the content. In the figure, the heat release rate of Nano-CaCO\({}_{3}\) decreases significantly in the later stage, which was conducive to the release of the hydration heat of the cement.  
Hydration heat at 28 d fluctuates at \(\sim\)450 J/g with various contents of Nano-CaCO\({}_{3}\), indicating that the addition of Nano-CaCO\({}_{3}\) does not affect the total amount of exothermic heat. Therefore, it could be inferred that nanoparticles were not directly involved in the hydration reaction. We therefore could infer that Nano-CaCO\({}_{3}\) would affect the mechanical properties of cement in the early stage of hydration.
## 5 Results and Discussion  
### Tuning of Validated DL Models  
To assess the relative performance of the proposed approaches, we conducted a comprehensive comparison against three convolutional neural network (CNN)-based deep learning models widely adopted in the literature. These include established architectures such as _ShallowConvNet_, _EEGNet_, and advanced variants like _TCNet Fusion_. Performance was evaluated using standard metrics--accuracy, kappa, and AUC--across a unified experimental protocol using the GigaScience MI-EEG dataset, as shown in Figure 5.  
As seen, _TCNet Fusion_[39] achieved the highest performance, attaining the top metric value with relatively low variability. This result indicates not only strong predictive capability but also consistent behavior across evaluation runs. _EEGNet_[38] ranked second, delivering solid performance accompanied by moderate dispersion. In contrast, _ShallowConvNet_[37] recorded the lowest performance and exhibited the highest variability, suggesting it is both less effective and less reliable for the given task. The use of Monte Carlo dropout led to performance improvements across all models reducing performance variability, with MCD _TCNet Fusion_ achieving the best overall results in both accuracy and stability. MCD _EEGNet_ also showed notable gains, while MCD _ShallowConvNet_ exhibited only slight improvement and remained the weakest model. Thus, MCD consistently enhanced both predictive accuracy and reliability, particularly in models with stronger baseline performance.  
Figure 5: Comparison of bi-class classification performance among state-of-the-art methods. Blue bars represent accuracy, orange bars denote Cohen’s kappa, and green bars indicate the area under the curve (AUC).
# Applications of GNSS-RO to Numerical Weather Prediction and Tropical Cyclone Forecast
### Principle of RO  
The RO technique is based on the atmospheric refraction phenomenon, which occurs when a GPS/GNSS signal penetrates the Earth's atmosphere and is then received by low-earth orbit (LEO) satellites behind the earth, as shown in Figure 1. During this refraction, the bending angle of the signal is considered a primary parameter in assimilation systems used for operational atmospheric research. The local and temporal atmospheric characteristics determine the bending angle \(\alpha\), which is principally subjected to the vertical gradient of the refractive index (\(n\)) along the penetrating path. The vertical gradient is associated with atmospheric parameters such as the temperature (\(T\)), total pressure (\(p\)), water vapor partial pressure (\(e\)), and electron number density [3]. This relationship can be expressed in the form of an empirical equation:  
\[N=n\times 10^{6}=77.6\frac{p}{T}+3.73\times 10^{5}\frac{e}{T^{2}} \tag{1}\]  
where \(N\) is the refractivity and \(n\) is the refractive index. Based on the assumption of local spherical symmetry, the bending angle can be transformed to refractive index \(n\) via Equation (2), i.e., the Abel transformation.  
\[n(r)=\exp\left[\frac{1}{\pi}\int_{a}^{\infty}\frac{\alpha(x)dx}{\sqrt{x^{2}-a ^{2}}}\right] \tag{2}\]  
where \(n\) (\(r\)) is the refractive index profiles, \(r\) is position vector, \(\alpha\) is the bending angle, and \(a\) is the impact parameter [3].
## 4 Experimental Section
### Confocal Imaging of Invasion  
The strains under analysis were grown overnight in YPD at 30 \({}^{\circ}\)C. The overnight cultures were used to inoculate 2 mL YPD+10%FBS at OD\({}_{600\mathrm{nm}}\) of 0.5 in wells, containing polyacrylamide discs pretreated overnight with fetal bovine serum (FBS). The cells were adhered to polyacrylamide by incubating for 90 min in an incubator-shaker at 60 rpm and 37 \({}^{\circ}\)C. After 90 min the cells not adhering to substrate were washed off with phosphate buffer saline (PBS) and fresh YPD + 10%FBS was then placed in each well. After an incubation for 48 h at 37 \({}^{\circ}\)C with 60 rpm agitation, the medium was aspirated out and the circular polyacrylamide discs, containing C. albicans biofilms, were fixed with 4% formaldehyde (30 min) and subsequently co-incubated with 0.185 mg/mL Calcofluor white and 0.05% green FluoSpheres in 1\(\times\)PBS (1 h). Roughly 1-2 mm sized strips were then cut across the disc diameter, with a sharp razor and the strip was laid over on its side on a cover-glass, housed within a culture dish. Serial stacks of images were acquired with a Zeiss LSM 510 Meta/DuoScan inverted spectral confocal microscope using a 40\(\times\)/1.2 NA-water immersion objective with the laser lines at 405 nm and 488 nm. The Zen 2009 software was used to obtain the desired \(Z\)-stack images. The serial image stacks were processed in FIJI ([[http://rsbweb.nih.gov/ij/](http://rsbweb.nih.gov/ij/)]([http://rsbweb.nih.gov/ij/](http://rsbweb.nih.gov/ij/))). The apical view projections were computed from the intensity corrected image stacks.  
For hyphal invasion depth measurement, we used the standard line measurement tool in FIJI. For 5-8 replicates, the depth was measured between the FluoSphere marked polyacrylamide surface and the longest hypha that invaded into the substrate. We performed the measurements in similar manner for the polyacrylamide discs of different elastic moduli.
###### Abstract  
This paper discusses the forced convective heat transfer characteristics of water-ethylene glycol (EG)-based Fe\({}_{3}\)O\({}_{4}\) nanofluid and Fe\({}_{3}\)O\({}_{4}\)-MWCNT hybrid nanofluid under the effect of a magnetic field. The results indicated that the convective heat transfer coefficient of magnetic nanofluids increased with an increase in the strength of the magnetic field. When the magnetic field strength was varied from 0 to 750 G, the maximum convective heat transfer coefficients were observed for the 0.2 wt% Fe\({}_{3}\)O\({}_{4}\) and 0.1 wt% Fe\({}_{3}\)O\({}_{4}\)-MWCNT nanofluids, and the improvements were approximately 2.78% and 3.23%, respectively. The average pressure drops for 0.2 wt% Fe\({}_{3}\)O\({}_{4}\) and 0.2 wt% Fe\({}_{3}\)O\({}_{4}\)-MWNCT nanofluids increased by about 4.73% and 5.23%, respectively. Owing to the extensive aggregation of nanoparticles by the external magnetic field, the heat transfer coefficient of the 0.1 wt% Fe\({}_{3}\)O\({}_{4}\)-MWNCT hybrid nanofluid was 5% higher than that of the 0.2 wt% Fe\({}_{3}\)O\({}_{4}\) nanofluid. Therefore, the convective heat transfer can be enhanced by the dispersion stability of the nanoparticles and optimization of the magnetic field strength.  
Keywords:Fe\({}_{3}\)O\({}_{4}\) nanofluid; Fe\({}_{3}\)O\({}_{4}\)-MWCNT hybrid nanofluid; forced convection; convective heat transfer; pressure drop; magnetic field +
Footnote †: journal: Applied
## 2 Materials and Methods
### Methodology  
The basic theory of the proposed algorithm is very similar to that of the CR method [16] and they are all developed for the arid areas with bright and stable surfaces. The assumption is that the land surface is very stable so that the variation of top of atmosphere (TOA) reflectance is completely induced by the variation of atmosphere alone. Subsequently, the CR method chooses one image least contaminated by the atmosphere from multi-temporal images as reference and the AODs for other images are retrieved by quantifying the variations induced by aerosols. Two problems immediately emerge while applying CR method to the MHSR data and the problems are described as follows:  
1. It is not easy to select the clearest images from multi-temporal images automatically;
2. For most of the Chinese MHSR data having very wide swath, the reflectance difference induced by BRDF is aggregated into the retrieved AOD while choosing one clear image as reference and the difference can reach as high as 15% in extreme cases [17]; the retrieved AOD is consequently degraded.  
In order to solve the above two problems for better retrieval of AOD from MHSR data, we propose a new algorithm which uses multi-year time series of MODIS data from both Terra and Aqua to construct the BRDF of arid areas with bright and stable surface in advance. The pre-constructed BRDF then supports the AOD retrieval from the MHSR data with much higher accuracy. The procedure of the proposed method is illustrated in Figure 1.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Sensor(s)** & **Platform(s)** & \
\begin{tabular}{c} **Spatial** \\ **Resolution (m)** \\ \end{tabular} & **Swath (km)** &
\begin{tabular}{c} **Spectra for** \\ **AOD Retrieval** \\ \end{tabular} & **Nation** \\ \hline TM, ETM+, & Landsat & 30 & 185 & VNIR, SWIR & USA \\ OLI & Sentinel-2 & 10/20/60 & 290 & VNIR, SWIR & ESA \\ MSI & HJ-1/A\&B & 30 & 700 (2 cameras) & VNIR & China \\ CCD & & 20 & 113 & \\ WFI & CBERS-02B & 2.36 & 27 & VNIR & China \\ CCD & & 8 & 60 & \\ WFV & GF-1 & 16 & 800 (4 cameras) & VNIR & China \\ MSI & ZY-3 & 5.8 & 52 & VNIR & China \\ CCD & & 8 & 60 & \\ WFV & GF-6 & 16 & 800 (1 camera) & VNIR & China \\ MODIS & Terra/Aqua & 250/500/1000 & 2330 & VNIR, SWIR & USA \\ \hline \hline \end{tabular}  
* Abbreviations: TM, Thematic Mapper; ETM+, Enhanced Thematic Mapper; OLI, Operational Land Imager; CCD, Charge-Coupled Device; WFI, World Field Imager; WFV, Wide Field Viewer; GF-1, GaoFen 1; ZY-1, ZiYuan 1; GF-6, GaoFen 6; AOD, aerosol optical depth; VNIR, Visible and near infrared bands; SWIR, short-wave infrared bands.  
\end{table}
Table 1: The details of the moderate to high spatial-resolution (MHSR) data are used in this study 1.
# More Time-Space Tradeoffs for Finding a Shortest Unique Substring
## 4 A Deterministic Algorithm  
**Lemma 1**.: _Given a length \(\ell\) and a position \(p\) in a text \(T[1..n]\) with alphabet size \(\sigma\), there exists a deterministic algorithm that can determine whether there is a substring of length \(\ell\) that covers position \(p\) and is unique in \(T\) in \(\mathcal{O}((\frac{\ell}{m}+1)n\log\sigma)\) time and \(\mathcal{O}(m)\) words of workspace._  
Proof of Lemma 1.: There are at most \(\ell\) possible substrings of length \(\ell\) covering \(p\) that could be unique. We separate these substrings into \(\lceil\frac{\ell}{m}\rceil\) batches of \(m\) substrings with adjacent starting positions. Note that there may be one remainder batch with less than \(m\) substrings.  
Consider a batch \(B\) containing \(k\) substrings of length \(\ell\), where \(1\leq k\leq m\). Let \(B_{i}\) denote the substring in \(B\) with the \(ith\) adjacent starting position in \(B\). Subsequently, \(B_{1}\) is the leftmost substring in \(B\) and \(B_{k}\) is the rightmost substring in \(B\).  
Consider the following substrings:  
\(x=B_{1}[1..k-1]\)  
\(z=B_{k}[\ell-k+2..\ell]\)  
\(y=B_{1}[k..\ell]=B_{k}[1..\ell-k+1]\)  
It is useful to think of \(x\) as the prefix of \(B_{1}\) that does not overlap with \(B_{k}\) in \(T\). Similarly, \(z\) is the suffix of \(B_{k}\) that does not overlap with \(B_{1}\). Finally, \(y\) is the suffix of \(B_{1}\) and the prefix of \(B_{k}\) that overlap with each other in \(T\). Note that any substring in \(B\) is equal to \(x_{suffix}\cdot y\cdot z_{pref}\), where \(x_{suffix}\) is some suffix of \(x\), \(z_{pref}\) is some prefix of \(z\) and \(\cdot\) denotes concatenation. For each batch, we will use the substrings \(x\), \(y\), and \(z\) to determine whether any of the \(k\) substrings in the batch occur in \(T\).  
We can scan \(T\) to enumerate all occurrences of suffixes of \(x\), all occurrences of \(y\), and all occurrences of prefixes of \(z\). Using this information we can determine if any of the \(k\) substrings in the batch occur elsewhere in \(T\) and are therefore not unique. Suppose that we search text \(T\) and we find an occurrence of a suffix \(q\) of \(x\), an occurrence \(r\) of \(y\), and an occurrence of a prefix \(s\) of \(z\). If \(q=T[i..j]\)\(r=T[j+1..j+1+\ell-k]\) and \(s=T[j+2+\ell-k..\ell+i-1]\) for some \(i,j\in[1,n],i\leq j\), then we have found an occurrence of a substring in batch \(B\) in \(T\). Specifically, if \(qrs=B_{a}\) for some \(B_{a}\in B\), then \(B_{a}\) is not unique in \(T\).
## 3 Sloshing Simulation
### Simulation Model  
A CVFEM method based on the Eulerian-Eulerian model was used to simulate the behavior of the fluid flow during the sloshing model test. An induced sloshing flow in the experimental condition is modelled using ANSYS CFX commercial Navier-Stokes VOF-CFD code [24]. The CFD code converts the governing differential equation to a set of algebraic equations by discretizing the fluid domain using constant volume based finite element method (CVFEM). The experimental conditions applied in this study are 2D experiments with regular sway motion conditions. However, the real experiments have produced in 3D conditions. Therefore, efficient 2D model was considered using 1-layer grid with symmetry conditions. The model test condition with a filling ratio of 50% of the tank height creates a violent impact in the upper edge under resonance conditions. Therefore, the computational grid for the wall of the tank was divided into small pieces using volume grids as shown in Figure 5. Furthermore, the sloshing model was defined  
Figure 4: Experiment apparatus (**a**) and sensor locations (**b**) for the sloshing model test.  
Figure 3: Comparison of pressure history of present study.  
using a commercial CFD program (ANSYS CFX) applied with VOF approaches and the RANS. In this work, grid dependency tests were conducted to examine the uncertainty of the sloshing CFD models.  
[PERSON] et al. obtained good results under grid conditions 128 by80 with the same sloshing conditions by CCUP Method [25]. In this study, sensitivity analysis was performed by varying the number of grids under the conditions of 64 by40, 128 by80, and 192 by120 to examine the same conditions in the commercial CFD code. When the simulation time and timestep were selected as 20 s and 0.001 s for each condition analysis. The analysis times of each analytical model were approximately 6 h, 40 h, and 62 h, respectively, in workstation environments using i7 intel CPUs. The 192 by120 grid condition was selected by the grid dependency test, and the total of grid of the sloshing CFD model used 23,040 hexahedral elements.  
Since the computational domain is completely enclosed, we have set the reference pressure by defining the center in the cell as shown in Figure 6. For evaluation of the proposed model, pressure signals are collected at the same positions as the model test. The atmospheric reference pressure is specified with reference to the center position node in the center of the top wall. The body force approach is applied to simulate tank motion. A standard k-e turbulence model was used in the CFD model.  
Figure 5: Computational grid (**a**) and boundary condition (**b**) for sloshing model test simulation.  
Figure 6: Comparisons of free surface profiles between model test and CFD: (**a**) Experiment; (**b**) CFD; (**c**) Experiment; (**d**) CFD; (**e**) Experiment; (**f**) CFD; (**g**) Experiment; (**h**) CFD; (**i**) Experiment; (**j**) CFD.
## 2 Related Works
### Igc
To this end, fault-tolerant control (FTC) greatly improves the adaptability to actuator faults, and it falls into two main categories: active FTC and passive FTC. Active FTC usually contains a fault detection and diagnosis (FDD) module [19] and a fault-tolerant controller design module, while passive FTC only contains the latter. In active FTC, faults are detected, isolated, and then estimated by the FDD module [20], which provides real-time information for the systems, and then the control strategy or even the controller is reconstructed. In this way, active FTC can deal with different types of faults effectively, but more sensors and computational capacity are required. Passive FTC needs no real-time information on faults and thus a faster response and simple structure are achieved, but it is usually effective only for known fault types. As the common module of active FTC and passive FTC, a well-designed fault-tolerant controller is important to improve the adaptability to actuator faults. For over-actuated systems or systems with redundant actuators, a control allocation-based fault-tolerant controller could significantly improve the reliability of the system. In [21], a closed-loop feedback integral-based allocation controller is proposed for an actuated flight vehicle to track the attitude command. The control moment is allocated to the variable span and sweep of morphing flight vehicles in [22]. For systems without additional power units, maintaining stability is the priority. In this case, actuator faults are always viewed as unpredictable dynamics or uncertainties, and then sliding mode control, adaptive control theories, and intelligent control are implemented to improve the robustness. A sliding mode fault-tolerant controller is designed in [23] to solve the trajectory tracking problem of flight vehicles with actuator faults. To enhance the robustness of aircraft control systems with actuator faults and disturbances, an adaptive fuzzy controller is designed in [24]. The proposed FTIGC combines the three-dimensional IGC and passive FTC techniques to deal with the disturbance and actuator fault.
## 3 Experimental Results and Analysis
### Ablation Study
#### 3.4.1 Assessment of the Proposed Framework's Effectiveness  
To gauge the effectiveness of our framework, we employed four mainstream networks (i.e., U-Net, ViT, TransUNet and ConvNeXt V2) as global feature extraction modules and compared their performance improvements within our framework on the AIDS and WBDS datasets. Table 2 presents the performance improvements of the proposed framework with different global feature extraction modules.  
Table 2 reveals that TransUNet outperforms U-Net, ViT and ConvNeXt V2 on both the AIDS and WBDS datasets. This demonstrates that TransUNet, chosen as our global feature extraction module, is effective. Moreover, adopting our proposed framework yields performance improvements over direct stitching results of U-Net, ViT, TransUNet and ConvNeXt V2, highlighting the effectiveness of our framework in utilizing the global characteristics of large-format images.
## 4 Segmentation Model Architecture  
The proposed architecture of the segmentation network is presented in Figure 7 composed of an encoder and decoder. It is a modified symmetric network based on U-Net [18]. The size of the feature map is more or less symmetrical in the pipeline of the encoder and decoder. The network consists of both \(3\times 3\) and \(5\times 5\) convolutional layers, each followed by a rectified linear unit (ReLu) and batch normalization. The \(2\times 2\) max-pooling layer is utilized for the downsampling instead of convolutions with a large stride, which ensures detailed information survives the downsampling process.  
The goal of the network is balancing the detection for all types of defects. Max-pooling downsampling layers and large kernel sizes would significantly increase the receptive field size. However, the downsampling would result in a loss of accurate spatial information. In order to obtain high resolution features, upsampling of the feature map by a \(2\times 2\) up-convolution is used, which halves the number of feature channels.  
The activation function is sigmoid:  
\[p_{ij}=sigmoid(s_{ij})=\frac{1}{1+\exp(-s_{ij})}, \tag{1}\]  
where \(s_{ij}\) is the output logical pixel. The cross-entropy loss function is widely used as follows:  
\[loss=\frac{1}{N\times M}\sum_{i=0}^{N-1}\sum_{j=0}^{M-1}\left(-[y_{ij}\times \ln p_{ij}+(1-y_{ij})\ln(1-p_{ij})]\right), \tag{2}\]  
Figure 6: Typical captured images: (**a**) Original glass image captured by the CBF system; (**b**) extracted images by 23 rows and 12 columns from original image in (**a**); (**c**) Original image captured by the LABF system; (**d**) extracted images by 23 rows and 12 columns from original image in (**c**).  
where \(N\) and \(N\) represent the width and height of the input source images, and \(p_{ij}\) and \(y_{ij}\) denote the example sigmoid regression prediction of the ground truth example annotation. The network can quickly obtain good performance in the relatively uniform regions; however, the segmentation result in the non-uniform edge regions is not so good. Therefore, the weight map based on local variance information is introduced. The region with a more complex texture would have a higher weight value. The local variance \(V_{ij}\) of pixel (_i,j_) on patch \(P\) is given by:  
\[V_{ij}=\frac{1}{Np}\sum_{x\in P}\left(x-\overline{x_{P}}\right)^{2}, \tag{3}\]  
where region \(P\) is centered on (_i,j_), \(x\) donates the pixel grayscale on \(P\), \(\overline{x_{P}}\) is the mean grayscale of patch \(P\), and \(N_{p}\) is the pixel number in \(P\). The weight map is introduced by:  
\[W_{ij}=\frac{V_{ij}+b}{\sum\limits_{i=0}^{N-1}\sum\limits_{j=0}^{M-1}\left(V_{ ij}+b\right)}, \tag{4}\]  
where \(b\) is the bias to balance the variance value. Therefore, the loss function would be the following formula:  
\[\textit{loss}=\sum\limits_{i=0}^{N-1}\sum\limits_{j=0}^{M-1}\left(-[y_{ij} \times\ln p_{ij}+(1-y_{ij})\ln(1-p_{ij})]\right)\times W_{ij}. \tag{5}\]