esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Numerical Model  
### Mathematical Equations  
The conservative form of the 2D shallow water equations, derived from the Navier-Stocks equations by considering the viscous, source, and friction terms are presented as follows:  
\[\frac{\partial\text{U}}{\partial\text{t}}+\frac{\partial\text{F}(\text{U})}{ \partial\text{x}}+\frac{\partial\text{G}(\text{U})}{\partial\text{y}}=\text{ S}_{\text{s}}+\text{S}_{\text{f}}+\text{S}_{\text{p}} \tag{1}\]  
where x and y represent the Cartesian coordinates; t is the time; U is the conservative variables; F and G are the flux vectors in the \(x\) and \(y\) directions, respectively. The bottom slope, the friction terms, and the source term are expressed by S\({}_{\text{s}}\), S\({}_{\text{f}}\), and S\({}_{\text{p}}\), respectively. The bottom slope, friction terms, source term, and flux vectors are defined as:  
* **The flux vectors F and G**: \[\text{F}(\text{U})=\begin{bmatrix}\text{hu}\\ \text{hu}^{2}+\frac{1}{2}\text{gh}^{2}\\ \text{hu}\end{bmatrix};\,\text{G}(\text{U})=\begin{bmatrix}\text{hv}\\ \text{huv}\\ \text{hv}^{2}+\frac{1}{2}\text{gh}^{2}\end{bmatrix}\] (2) where h is the water height; g is the gravitational acceleration; u and v are the depth-averaged velocity components in the \(x\) and \(y\) directions.
* **The bottom slope S\({}_{\text{s}}\), friction S\({}_{\text{f}}\), and source S\({}_{\text{p}}\)**: \[\text{S}_{\text{s}}=\begin{bmatrix}0\\ -\text{gh}\frac{\partial\text{z}}{\partial\text{y}}\\ -\text{gh}\frac{\partial\text{z}}{\partial\text{y}}\end{bmatrix};\,\text{S}_{ \text{f}}=\begin{bmatrix}0\\ -\text{nn}^{2}\frac{\sqrt{\text{u}^{2}+\text{v}^{2}}}{\text{h}^{\frac{3}{2}}} \text{u}\\ -\text{nn}^{2}\frac{\sqrt{\text{u}^{2}+\text{v}^{2}}}{\text{h}^{\frac{3}{2}}} \text{v}\end{bmatrix};\,\text{S}_{\text{p}}=\begin{bmatrix}\text{P}-(\text{I}- \text{E}-\text{q})\\ 0\\ 0\end{bmatrix}\] (3) where \(-\frac{\partial\text{z}}{\partial\text{x}}\) and \(-\frac{\partial\text{z}}{\partial\text{y}}\) are the bed slopes in the \(x\) and \(y\) directions, respectively; z is the bed elevation; nm is the Manning-Strickler coefficient; P and I are the rainfall intensity (mm/min) and infiltration rate; E is the surface evaporation (mm/min) and q is the flow rate. [PERSON]'s infiltration model, developed in the 1930s, has been used to calculate the infiltration rate [37]. The model indicates that infiltration tends to decrease exponentially if the rainfall exceeds the infiltration capacity. The formula is expressed as: \[\text{I}(t_{s})=\text{i}_{\text{f}}+(\text{i}_{0}-\text{i}_{\text{f}})\exp(- \text{r}t_{s})\] (4) where i\({}_{0}\), i\({}_{\text{f}}\), r, and \(t_{s}\) are, respectively, the initial infiltration rate (mm/min), the final infiltration rate (mm/min), a constant depending on soil properties (min\({}^{-1}\)), and the duration of the storm (min).
# Efficient Inorganic Stabilization Materials for Chromium and Arsenic Pollution in Water and Soil
## 4 Factors Affecting the Stabilization Process of Chromium and Arsenic Stabilized Materials
### The Influence of Coexisting Ions on the Stability Process of Chromium and Arsenic  
In polluted sites, there may be other ions present in water or soil. To some extent, they will also affect the effectiveness of the stabilization process. Some ions, due to their similar properties to chromium or arsenic ions, will first react with the stabilizing material, occupying the active sites on the surface of the material and causing competitive adsorption, resulting in a decrease in the stabilization efficiency of chromium or arsenic. Some ions react with functional groups on the surface of the material to form new groups that promote the stability of chromium or arsenic. Therefore, before applying stabilization materials, it is necessary to investigate the influence of coexisting ions on their stabilization effect. The [201]TiO\({}_{2}\)-ZrO\({}_{2}\) composite materials were prepared by [PERSON] et al. [105] and were used for As removal in drinking water. Under acidic conditions, SO\({}_{4}\)\({}^{2-}\) and PO\({}_{4}\)\({}^{3-}\) inhibited the adsorption of 20-30% As(III). Under alkaline conditions (pH greater than 9), SO\({}_{3}\)\({}^{2-}\) and CO\({}_{3}\)\({}^{2-}\) also have inhibitory effects. For As(V), Mg\({}^{2+}\) and Ca\({}^{2+}\) increase removal efficiency by 20% at pH value greater than 10 (Figure 12e). [PERSON] et al. [106] prepared a mixture of CMC-FeS@EPS (carboxymethyl cellulose-stabilized ferrous sulfide@extracellular polymeric substance) from CMC (carboxymethyl cellulose), EPS (extracellular polymeric substance), and FeSO\({}_{4}\) 7H\({}_{2}\)O to remove Cr(VI). The addition of Ca\({}^{2+}\), Mg\({}^{2+}\), and Na\({}^{+}\) changed the surface charge characteristics of the material or bound with functional groups, which resulted in a decrease in the removal rate of Cr(VI) from 89.67% to 65.33%, 66.46%, and 74.42%. A precipitate was formed by HCO\({}_{3}\)\({}^{-}\) with FeS, which reduced the removal rate to 56.67%. Electrostatic repulsion with chromium ions was enhanced by SO\({}_{4}\)\({}^{2-}\) and NO\({}_{3}\)\({}^{-}\), and the removal rates were decreased to 72.46% and 78.83%, respectively (Figure 12b). [PERSON] et al. [107] prepared MnS-encapsulating biochar-dispersed zero-valent iron (MnS-nZVI@BC). Adding different interfering ions, except for Na\({}^{+}\), other cations (K\({}^{+}\), Cu\({}^{2+}\), Ni\({}^{2+}\), Ba\({}^{2+}\), Ca\({}^{2+}\), Cd\({}^{2+}\), Cr\({}^{3+}\), and Zn\({}^{2+}\)) promoted the stability of Cr(VI) due to increased solution conductivity or enhanced surface positive charge. The addition of Cl\({}^{-}\) and NO\({}_{3}\)\({}^{-}\) had no effect on the stabilization process, while SO\({}_{4}\)\({}^{2-}\) promoted removal due to the enhanced electrostatic attraction between Cr(VI) and the material (Figure 12c).
## Attosecond Hard X-ray Free Electron Laser  
**[PERSON] 1, [PERSON] 1 and [PERSON] 2,3,**  
* [1] Pohang Accelerator Laboratory, San 31, Hyoja-dong, Pohang, Kyungbuk, 790-784, Korea; E-Mails: [EMAIL_ADDRESS] (S.K.); [EMAIL_ADDRESS] (H.-S.K.)
* [2] Department of Physics, Center for Attosecond Science and Technology (CASTECH), Pohang University of Science and Technology (POSTECH), San 31, Hyoja-dong, Pohang, Kyungbuk, 790-784, Korea
* [3] Max Planck Center for Attosecond Science, Max Planck POSTECH/KOREA Research Initiative, San 31, Hyoja-dong, Pohang, Kyungbuk, 790-784, Korea
* [*] Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel./Fax: +82-54-279-2089.  
_Received: 10 December 2012; in revised form: 24 February 2013 / Accepted: 25 February 2013 / Published: 12 March 2013_  
**Abstract:** In this paper, several schemes of soft X-ray and hard X-ray free electron lasers (XFEL) and their progress are reviewed. Self-amplified spontaneous emission (SASE) schemes, the high gain harmonic generation (HGHG) scheme and various enhancement schemes through seeding and beam manipulations are discussed, especially in view of the generation of attosecond X-ray pulses. Our recent work on the generation of attosecond hard X-ray pulses is also discussed. In our study, the enhanced SASE scheme is utilized, using electron beam parameters of an XFEL under construction at Pohang Accelerator Laboratory (PAL). Laser, chicane and electron beam parameters are optimized to generate an isolated attosecond hard X-ray pulse at 0.1 nm (12.4 keV). The simulations show that the manipulation of electron energy beam profile may lead to the generation of an isolated attosecond hard X-ray of 150 attosecond pulse at 0.1 nm.  
**Keywords:** attosecond pulse generation; X-ray free electron laser; undulator radiation
## 4 Experimental Results
### Experiments
As we can see in Figure 5(a), the SOM algorithm needs much less redundancy to work compared to the linear or beta algorithms, while obtaining values of system degradation similar to the other algorithms 5(b). Since node trusts have no influence in the SOM algorithm, it should be more resistant to reputation attacks like badmouthing. This experiment proves that it is fulfilled. The experiment entails a badmouthing sybil node that attacks the system. The attack decreases the reputation of a group of nodes surrounding the attacker. When their reputation is low enough, the information from sybil identifiers will be used for the decisions. The trust of the neighbors about the attackers will not be taken into account due to their low reputation and, hence, the sybil will not be confined (Figures 6(a) and 6(b)). However, in Figure 6(c) it is shown that the SOM algorithm imperturbably detects and banishes the sybil attack. The scenario of the experiment has consisted of 180 badmouthing nodes in a small region and, after a while, these nodes become into a sybil node that adopt the identities of the badmouthing nodes. Figure 5: Evolution of the impact of the attack and the based on the sensor node redundancy.
## 3 Data Description  
### The inD Dataset  
Data compilation for the inD dataset involved meticulous drone video recordings at multiple urban intersections in Aachen, Germany, over the period from 2017 to 2019. It captures the movements of 13,599 road participants, encompassing a diverse mix of 8233 vehicles (including cars, trucks, and buses) and 5366 vulnerable road users, such as pedestrians and bicyclists. This rich collection of trajectory data spans across various urban traffic scenarios [45].  
Figure 7: The sigmoid function and the transformed sigmoid function.  
The data collection was conducted at four different locations, with each session typically lasting approximately 20 min. The scope of these recordings varied from intersection areas of 80 \(\times\) 40 m to 140 \(\times\) 70 m. In this dataset, each video record includes a background image, a map, and three trajectory information files. The files encompass trajectory information with the type of road user, position, direction, speed, and acceleration. A sample frame from the inD dataset is illustrated in Figure 8. As video data are collected at complex traffic intersections, the inD dataset is frequently used in the research of traffic conflict techniques. Due to the complex environment and traffic flow characteristics of urban intersections, they usually have more types and numbers of conflicts. Therefore, this study uses the inD dataset for the effective verification of PCRI in Section 4.
## 4 Discussion
### Model Applications and Limitations  
Our model was developed with a robust biological basis with long-term and inter-sites observations, and it performed well in estimating \(RE\) for northern China's grasslands solely driven by MODIS products. The main advances introduced by this model are the better representation of the _Rerf_ process and the comprehensive consideration of moisture effect combined with temperature and productivity effects on \(RE\). The derived parameters reported in Table 2 may be considered as an optimized parameterization for the application of the model at a regional scale. Our model may facilitate the simulation of \(RE\) at larger spatial scales with a high temporal resolution and could also  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Vegetation Type**} & \multicolumn{2}{c}{**Model without _fw_**} & \multicolumn{2}{c}{**Model with _fw_**} \\ \cline{2-5}  & \(R^{2}\) & **RMSE (g C m\({}^{-2}\) day\({}^{-1}\))** & \(R^{2}\) & **RMSE (g C m\({}^{-2}\) day\({}^{-1}\))** \\ \hline Typical steppe & 0.68 & 0.70 & 0.74 & 0.61 \\ Desert steppe & 0.78 & 0.22 & 0.97 & 0.13 \\ Meadow steppe & 0.94 & 0.35 & 0.95 & 0.34 \\ Alpine _Kobresia_ meadow & 0.75 & 1.09 & 0.78 & 0.93 \\ Alpine meadow steppe & 0.71 & 0.21 & 0.81 & 0.16 \\ Alpine swamp meadow & 0.69 & 0.77 & 0.72 & 0.74 \\ Alpine shrub meadow & 0.86 & 0.74 & 0.88 & 0.72 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of model performance between the model without _fw_ and the model with _fw_ for each vegetation type.  
be combined with the satellite-based GPP models or MODIS-retrieved GPP products to conduct a regional-scale NEE simulation.  
However, the further application of our model at a larger scale probably has several limitations: (1) The spatial variability (_Reref_) is explained to a relatively lower degree compared to the temporal variability (_f_(_T_,\(W\),_P_)). Since _RE_ is also influenced by a number of other biotic and abiotic factors, such as age of the ecosystem ([PERSON], 2017), nutrient availability ([PERSON], 2017), and acclimation and disturbance effects, we expect that more comprehensive information could be incorporated in the model to better represent the variations of _Reref_; (2) Although the satellite-derived VIs can to some extent reflect the impacts of GPP on respiration ([PERSON], 2017; [PERSON], 2017) and have also been widely used to explain the seasonal variation in Rs as VIs are related to soil carbon through litter input ([PERSON], 2017; [PERSON], 2017), they are still difficult to mirror in terms of the influence of the belowground organic matter on _RE_; (3) The performance of our model for other vegetation types or in other regions needs to be validated in further study. Without further testing, the model is strictly applicable to similar ecosystems (i.e., temperate and alpine grassland ecosystems). Along with the development of satellite algorithms for soil moisture, plant biomass, nutrient availability, human activity, and so on, we will optimize the model to improve its accuracy.
## 3 Test Results and Discussion
### Binder Properties
#### 3.1.5 Calicined Clay and Limestone Morphology  
The morphology of the calcined clay samples (EXW, BFN, MFN) and limestone investigated by SEM is detailed in this section. After calcination, amorphous metakaolin (MK) products (Al\({}_{2}\)O\({}_{7}\)Si\({}_{4}\)) are formed, and the major elements to identify are aluminium, silicon, and oxygen, confirmed by EDS by taking spectra of particles from the SEM micrographs.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Alite** & **Belite** & **Ferrite** & **Aluminate** & **Sulphates** & **Gypsum** & **Bassanite** & **Calcite** & **Quartz** \\ \hline C\({}_{5}\)S & C\({}_{2}\)S & C\({}_{4}\)AF & C\({}_{3}\)A & (K, Na)\({}_{2}\)SO\({}_{4}\) & CaSO\({}_{2}\)H\({}_{2}\)O & 2 CaSO\({}_{2}\)H\({}_{2}\)O & CaCO\({}_{3}\) & SiO\({}_{2}\) \\
75.7 & 0 & 4.86 & 1.26 & 0.14 & 0.07 & 0.72 & 16.11 & 1.21 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Mineral composition of the Portland cement.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline
**Sample** & **Al\({}_{2}\)O\({}_{3}\)** & **CaO** & **Cr\({}_{2}\)O\({}_{3}\)** & **Fe\({}_{2}\)O\({}_{3}\)** & **K\({}_{3}\)O** & **MgO** & **MnO** & **Na\({}_{2}\)O** & **P\({}_{2}\)O\({}_{3}\)** & **SiO\({}_{2}\)** & **TiO\({}_{2}\)** & **LOI** & **Sum of Conc.** & **PO** \\
**Name** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** \\ \hline MFN & 21.86 & 0.12 & 0.01 & 0.74 & 2.72 & 0.23 & 0.01 & 0.19 & 0.01 & 72.27 & 0.80 Investigating the morphology of the samples helps to elucidate the physical shapes, texture, and clustering of the particles as they relate to the PSD in Section 2.1.4[13].  
Figure 5a-c shows the particle morphology of the EXW, BFN, and MFN clay samples, respectively. The EDS analysis of the selected particles' area spectra (average of about 28 spectra) is shown in Table 7. Figure 5 shows that the clay particles are generally flat and irregular with kaolinite booklets; EXW, in particular, tends to be elongated. The texture of the particles also tends to be fibrous. These shapes and textures are expected to reduce their workability in the mortar mixture [29]. The morphology of the limestone particles is shown in Figure 6, and the particles tend to be flat and prismatic. With the fineness of limestone obtained in Section 3.1.1 and morphology obtained here, it is expected that the particles should be able to serve as fillers for improving the strength of the mortar samples. Table 7 shows that the main elements are aluminium, silicon, and oxygen, supporting the earlier XRF results (Table 4).
# An Effective Off-Grid DOA Estimation Algorithm Using Difference Coarrays with Limited Snapshots
## 5 Simulation Results
### Convergence Performance for NLA  
Finally, four off-grid sources coming from \(\theta_{1}=0.5^{\circ}\), \(\theta_{2}=10.5^{\circ}\), \(\theta_{3}=20.5^{\circ}\), and \(\theta_{4}=30.5^{\circ}\) and 20 snapshots were utilized in the convergence evaluation. The RMSE of the proposed method converged within three iterations across all of the tested SNRs, as shown in Figure 12, with further iterations yielding no improvement.  
Figure 11: RMSE of the four methods versus source separation with 10 dB SNR for NLA.  
Figure 12: RMSE of the proposed methods versus the iteration number with different SNR.  
Figure 10: RMSE of the four methods versus source separation with 0 dB SNR for NLA.
## 4 Discussion
### Effect of Dam Material Particle Size on the Experimental Parameters  
The particle size of the dam material affects the porosity and compaction density of the dams, resulting in different sediment depositions inside the pores of the dam body, which leads to differences in discharge capacity and sediment interception performance. From the analysis of the experimental results, it can be seen that the amplitude and rate of water level rise in front of the dams with a small particle size is greater than that of the dams with a large particle size, and they vary regularly with the discharge and sediment concentration. After the beginning of the experiments, the porosity of the dam gradually decreased, and the permeability coefficient of both large and small particle size dams decreased to different degrees.  
For the small particle size, the difference between the turbidity at the inlet and outlet of the flume was larger and the total turbidity removal rate of the dam was higher than that of the large particle size. When the permeable dams are first put into use, the pore volume of the dam body is large and the sediment migration affects the change in the pore structure, resulting in differences in the intercepted sediment mass in each dam body of large and small particle sizes. As shown in Figure 9, the total mass of intercepted sediment in the permeable dam with a large particle size is significantly larger than that with a small particle size. Because the dams with a large particle size can work for a longer time and deal with a larger volume of flood, the amount of sediment deposition is affected by the charge [51].  
In order to describe the sediment interception efficiency of the dams more directly, at the end of the experiments, the intercepted sediment mass in 1-4\(\mu\) dam was divided by the total water volume and the effective mass of the dam material in each dam to obtain the sediment interception rate (\(\eta\)) for comparison. As shown in Figure 10, the \(\eta\) of the small particle size is significantly greater than that of the large particle size, and decreases with increasing discharge. The \(\eta\) of 1\(\mu\) dam is significantly greater than that of the subsequent dams, and is greatly influenced by the sediment concentration. The difference in \(\eta\) between 2\(\#\), 3\(\#\), and 4\(\#\) dam is smaller, especially at 700 L/H. Since the variation in the parameters for the small particle size is more regular and more in line with the expected results, only the relevant parameter variation of the permeable dams with the small particle size permeable dam will be covered in the following discussion.
# Adaptive DecayRank: Real-Time Anomaly Detection in Dynamic Graphs with Bayesian PageRank Updates
## 5 Experiments
### Scalability and Robustness
#### 5.4.2 Scalability  
Figure 6 shows how well Adaptive-DecayRank scales with increasing edges across four datasets. We plot the wall-clock time required to process the first \(2^{1},2^{2},\ldots,2^{22}\) edges of each dataset. On **DARPA**, which contains 4.6\(M\) edges and 1463 snapshots, the elapsed time for _small inputs_\((10,100)\) remains in _microseconds_, demonstrating Adaptive-DecayRank's efficiency for small graphs. At _medium sizes_ (1000-10,000), the elapsed time remains relatively low (_milliseconds_), confirming its ability to handle moderate-sized graphs effectively. However, for _larger inputs_ (100,000-1,000,000), the elapsed time increases slightly from **0.77 to 14.23** s, indicating higher computational requirements for large-scale graph updates. On **CTU-13**, which contains 1.4\(M\) edges and 301 snapshots, the algorithm processes small graphs efficiently (_microseconds_ for 10, 100), and even for the largest input size (1,000,000), it completes in **0.1057** s, reinforcing its scalability for real-time applications. On **RTM-30**, which includes 19\(M\) edges and 101 snapshots, processing times remain efficient, with **0.08** s required for 1,000,000 edges, demonstrating near real-time anomaly detection performance. Across datasets, Adaptive-DecayRank scales efficiently, maintaining _microsecond-level_ times for small graphs, _millisecond-level_ times for moderate-sized graphs, and handling _larger graphs in seconds_. Future work will explore how _graph structure_ (e.g., density, connectivity) influences runtime performance.
# Computational Tools and Resources Supporting CRISPR-Cas Experiments
## 3 Guide RNA Design
Similar results were presented by [PERSON] et al., who compared predictions of Cas-OFFinder, CCTop and the off-target sites identified by Site-seq [69]. All these issues led, similarly to the problem of gRNA activity, to the development of more advanced scoring tools that aim to accurately identify the most likely off-target sites [10; 16; 61; 70; 71]. The algorithms were built on data from screening studies focused on the relationship between the observed Cas9 activity and the number and distribution of mismatches. The most widely applied algorithms are the MIT specificity score and the Cutting Frequency Determination (CFD) score [10; 16]. Since all these tools were developed on the basis of different data-sets and differ in the way that they score particular factors, it is advisable to compare results generated by different algorithms. Certain tools implement more than one predictive algorithm, allowing the user to choose the most appropriate approach (CRISPRseek, CRISPRscan, CRISPR Multi-Targeter, CRISPOR). The majority of available tools still use only the basic sequence features to predict off-target activity of a given gRNA. The involvement of more complex characteristics, such as genomic context, chromatin environment, DNA/RNA thermodynamics, and the occurrence of DNA or RNA bulges, is ambiguous and not fully understood. There are, however, attempts to incorporate these factors into predictive models, e.g., CROP-IT, DeepCRISPR, uCRISPR or CRISTA [72; 73; 74; 75]. Among tools facilitating the design of efficacious and specific sgRNAs, we recommend CRISPOR. It integrates the Doench score, in our opinion one of the most robust efficiency prediction systems, along with many other algorithms, allowing a direct comparison of their results, making the prediction more reliable. It supports 15 nucleases and more than 150 genomes. CRISPR provides sequences for in vitro expression or cloning of a given sgRNA using different systems as well as primers for amplification of on- and off-target sites. The tool integrates also the [PERSON] et al., 2014 predictions of repair profile and the [PERSON] et al., 2018 frameshift prediction [5; 76]. Moreover, CRISPR warns the user when a designed gRNA contains motifs that should be avoided as described by [PERSON] et al. [77]. In Supplementary Table S1, we present a summary of the most popular tools for gRNA design.