esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Limitations and Potential Advancements  
The approach provides a reliable prediction of the tail end of the response statistics. Yet, we tested this strategy on two-dimensional models only. Three-dimensional structures will be of high interest for this strategy as the number of degrees of freedom drastically increases and, consequently, the computational cost. In this case, the accuracy of the proposed approach must be tested. Furthermore, we used a rather simple assumptionfor the criterion of structural failure. Incorporating more elaborate material models that include material damage and softening will result in a significantly higher computational cost. It is interesting to evaluate the computational speed up in this case since we assume that the neural network prediction will not be affected by this extension. However, in this study, we used a bi-linear material model and analyzed the behavior in the cross section with the highest stresses and focused on the level of plastification. Evaluating static cyclic test calculations one observes that, if the story drift ratio exceeds the chosen failure criterion of a maximum PSDR the most affected cross section, i.e., the cross section in the first frame corner, fully plastifies.
## 2 BIT* Algorithm Procedure
### Improved BIT* + Energy Consumption Equation + TD3 Hybrid Path Planning Strategy Model  
This research proposes a hybrid path planning strategy suitable for complex environments, integrating an enhanced BIT* algorithm and an energy consumption equation. By leveraging mathematical modeling, ellipsoidal sampling, and a sequential node optimization mechanism, this approach reduces computational complexity and optimizes path planning in dynamic environments. The strategy prioritizes nodes in the early segment of the path, thereby improving the navigation efficiency and accuracy of Unmanned Surface Vehicles (USVs). Concurrently, a path pruning mechanism eliminates invalid paths, ensuring the practical application of the navigation scheme.
## 2 Materials and Methods
### Column Experiments--Operation System
#### 2.2.2 Joint Infiltration of TWWE and Additional Water Originating from Precipitation  
The second experimental phase simulated the joint infiltration of TWWE and additional water to investigate the influence of these events on the biocenosis and the purification efficiency. Additional water, in accordance with intensive precipitation events, was applied to the test columns.  
Different precipitation events, based on statistical recurrence interval of 5 years event (Table 3) (rated precipitation according to [28]) at Schoneck/Vogtland were simulated after achieving stable degradation of infiltrated residuals in the first experimental phase.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Type of Water** & **pH (-)** & **EC (\(\mu\)S cm\({}^{-1}\))** & **COD** & **NO\({}_{2}\)-N** & **NO\({}_{3}\)-N** & **NH\({}_{4}\)-N** & **PO\({}_{4}\)-P** \\ \hline TWWE & 7.1 \(-\) 7.7 & 960 \(-\) 1080 & 49 \(-\) 120 & n.d. \(-\) 3.2 & 3 \(-\) 57 & 3 \(-\) 82 & 18 \(-\) 44 \\ PW & 6.2 & 58 & n.d. & n.d. & 2 & n.d. & n.d. \\ Limit value [27] & 6.5 \(-\) 9.5 & 2790 & n.a. & 0.15 & 11.3 & 0.39 & n.a. \\ \hline \hline \end{tabular} Notes: n.d.—not detectable, n.a.—not available.  
\end{table}
Table 2: The used TWWE (mean values) and precipitation water (PW).  
Intensities of different precipitation event durations were acquired by using data from [29]. This dataset is based on measured values of precipitation for a period between 1951 and 2010 and is valid for different reference sites in Saxony, Germany.  
Based on the hydraulic conductivities of the used soils 1, 3, and 4, a 5-year precipitation event had a length of 30 min and an intensity of 136 L s\({}^{-1}\) ha\({}^{-1}\) (Table 4, event 1). Due to the lower hydraulic conductivity, an event length of 120 min with an intensity of 51.6 L s\({}^{-1}\) ha\({}^{-1}\) was used for soil 2 (Table 4, event 1). This resulted in a loading rate per column surface area of 14.4 mL min\({}^{-1}\) (column 1, 3, 4) and 5.5 mL min\({}^{-1}\) (column 2). These amounts were infiltrated directly on the top soil layer, representing additional infiltration from precipitation. Subsequently, event durations and intensities were increased to simulate worst-case events (Table 4, events 2 and 3) for investigating the effects on the purification efficiency caused by higher water content, on the one hand, and longer periods with increased water content, on the other hand.  
The infiltrated synthetic precipitation prepared on the basis of demineralized water contained 6.86 mg L\({}^{-1}\) NaNO\({}_{3}\), 1.64 mg L\({}^{-1}\) KHCO\({}_{3}\), and 26.96 mg L\({}^{-1}\) CaSO\({}_{4}\). Corresponding concentrations of the most important quality parameters are shown in Table 2. Between two consecutive precipitation events, only TWWE was infiltrated for at least two weeks to provide sufficient time for the microorganism community to recover.
## 3 Proposed System Model
### Factors for Node Importance Calculation  
This problem takes into account four crucial aspects that might have an impact on every node in a complex network. The NIRA of nodes in a network is created depending on the value of the variables that have been determined for them. It is derived from the basic complex network theory that the formulations for the factors are derived.  
* Node Capacity Factor (NCF): The availability of the number of CPUs on the node is used to determine the capacity of the node. A node with a higher number of CPUs may give service to a greater number of NSRs. As a result, a node with a large number of CPUs should be given a better ranking in the NIRA system. The value of the factor is updated immediately upon the accomplishment of each NSR as below: \[f_{NCF,i}(t)=CPU_{available,i}(t)-CPU_{NSR,i}(t)\] (19) where \(CPU_{available,i}(t)\) is the available CPU capacity of the \(i^{th}\) node of physical infrastructure at time \(t\), and \(CPU_{NSR,i}(t)\) is the requested CPU capacity of the \(i^{th}\) node of the NSR received at time \(t\).
* Node Topology Factor (NTF) and Node Bandwidth Factor (NBF): In both cases, the number of adjacent connections present with the physical node determines the NTF and NBF. The total number of adjacent connections of a node is represented by NTF, whereas the total number of adjacent links bandwidth is represented by NBF. Any node with a greater NTF and NBF is given a higher priority in the NIRA. Each NSR results in an update of the factors. The equations for updating the factors are shown below: \[f_{NTF,i}(t)=\sum_{j=1}^{tNades}a_{ij,available}(t)\] (20) \[f_{NBF,i}(t)=\sum_{j=1}^{tNades}(BW_{available,ij}(t)-BW_{NSR,ij}(t))\] (21) where \(tNodes\) is the total nodes present in the network, \(a_{ij,available}(t)\) is 1 if the \(i^{th}\) node has a connection with \(j^{th}\) node and unserved at time \(t\), and 0 otherwise, \(BW_{available,ij}(t)\) is the available bandwidth of the link connecting node \(i\) and node \(j\) of physical infrastructure at time \(t\), and \(BW_{NSR,ij}\) is the requested bandwidth of the link connecting node \(i\) and node \(j\) of the NSR at time \(t\).
* Node Closeness Centrality Factor (NCCF): Using the factors NCF, NTF, and NBF, we may learn about the node's local information. A node's strength in a network should be measured by how much it can influence the shortest path. A node's global relevance is determined by its shortest route information, which is provided by NCCF. Therefore, the closer a node is to other nodes, the greater its centrality. Listed below is the NCCF equation at time \(t\). \[f_{NCCF,i}(t)=\{\sum_{j=1}^{nNodes}L_{i,j}\}^{-1},i\
eq j\] (22) where \(L_{i,j}\) is the shortest path length between node \(i\) and \(j\).
# A Novel Efficient Borehole Cleaning Model for Optimizing Drilling Performance in Real Time
## 2 The Influence of Factors on Borehole Cleaning
### The Main Models to Evaluate the borehole Cleaning Efficiency
flow rate, and angle factor, which are crucial in evaluating the overall borehole cleaning efficiency during drilling operations [37]. More importantly, there are several models, tools, chemicals, charts, methodologies and correlations that were used; however, they were not applicable, inefficient, costly, and not feasible with drilling operations such as CCA\({}_{1}\)[35]. Moreover, the models utilized to assess the drilling performance and the borehole cleaning conditions have several significant inadequacies. First, they do not consider the mud weight in both static and dynamic conditions, including equivalent circulating density. Second, there are other critical factors that they do not consider, including hydraulic velocities, rheological properties of drilling fluids (including the low shear yield point), flow regime, and cuttings properties, which can all have an impact on the drilling process. In summary, each model provides a unique perspective on evaluating the cutting transport and borehole cleaning conditions during drilling operations, with the TI model being the most comprehensive and inclusive among them. Addressing these shortcomings can lead to more accurate and reliable predictions, ultimately improving the safety and efficiency of drilling operations. Therefore, in the next section, we present a newly developed model called the transport index based on [PERSON]' 1992 and 1994 [37; 38], which provides a comprehensive evaluation of various factors impacting borehole cleaning. The novel model is designed to assist drilling teams in optimizing drilling operations by empowering them to make informed decisions about necessary modifications based on a thorough understanding of the underlying  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \(\mathbf{N}\) & **Name of Model** & **Equations** & **Definition** & **Ref. ** \\ \hline \multirow{8}{*}{1} & & & The HCR is a ratio between the & \\  & & & free height of the drilling fluid in & \\  & & & the annulus and the critical & \\  & & & height of the cuttings bed, and it & \\  & & & is commonly utilized to assess & \\  & & & the risk of a pipe becoming stuck & \\  & & & during drilling operations. & \\  & & & The CCI is used to indicate the & \\  & & & borehole cleaning efficiency in & \\  & & & vertical borehole sections. & \\  & & & The CCA\({}_{1}\) is commonly utilized & \\  & & & to assess the cessation of & \\  & & & circulating during connections & \\  & & & and the circulation which occurs & \\  & & & prior to a connection. & \\  
factors. By utilizing the novel model, drilling teams can identify potential issues and make necessary modifications in a timely and efficient manner, ultimately leading to improved safety, efficiency, and cost effectiveness. This model is particularly valuable in reducing the need for expensive additives and chemicals in the drilling fluid system, which can significantly impact the overall cost of drilling operations.
# Chinese Document-Level Emergency Event Extraction Dataset and Corresponding Methods
###### Abstract  
Structured extraction of emergency event information can effectively enhance the ability to respond to emergency events. This article focuses on extracting Chinese document-level emergency events, which entails addressing two key issues in this field: related datasets and the problem of role overlapping between candidate entities, which has been overlooked in existing DEE (document-level event extraction) studies that predominantly employed sequence annotation for candidate entity extraction. To tackle these challenges, we constructed a Chinese document-level emergency extraction dataset (CDEE) and provides annotations for argument scattering, multiple events, and role overlapping. Additionally, a model named RODEE is proposed to address the role overlapping problem in DEE tasks. RODEE employs two independent modules to represent the head and tail positions of candidate entities, and utilizes a multiplication attention mechanism to interact between the two, generating a scoring matrix. Subsequently, role-overlapping candidate entities are predicted to facilitate the completion of DEE tasks. Experiments were conducted on our manually annotated dataset, CDEEE, and the results show that RODEE effectively solves the problem of role overlapping among candidate entities, resulting in improved performance of the DEE model, with an F1 value of 77.7%.  
candidate entity extraction; document-level event extraction; role overlapping
## 3 Results
### Antioxidant Activities of BR, BRB, and MBR  
This study evaluated the antioxidant activities of BR, BRB, and MBR in different extraction solvents using in vitro antioxidant activity assays, including ABTS free radical scavenging, DPPH free radical scavenging, and FRAP activity assays. The results of the  
Figure 1: HPLC chromatograms of \(\gamma\)-oryzanol in the 80% EtOH and DW extracts of black rice (BR), black rice bran (BRB), and milled black rice (MBR). (**A**) HPLC chromatograms of the \(\gamma\)-oryzanol standard; (**B**) 80% EtOH extract of BR; (**C**) 80% EtOH extract of BRB; (**D**) 80% EtOH extract of MBR; (**E**) DW extract of BR; (**F**) DW extract of BRB; and (**G**) DW extract of MBR.  
DPPH free radical scavenging activity assay revealed that the scavenging activities of the 80% EtOH extracts of BR, BRB, and MBR at a sample preparation concentration of 1 mg/mL were 64.02 \(\pm\) 0.92, 59.3 4 \(\pm\) 1.02, and 41.04 \(\pm\) 1.30%, respectively. These values are superior to those of the DW extracts, with the BR extract exhibiting the highest antioxidant activity among the samples (Figure 2A). Furthermore, in the ABTS free radical scavenging activity assay, the scavenging activity of the 80% EtOH extract at a concentration of 1 mg/mL was 81.49 \(\pm\) 0.26% for BR, 80.61 \(\pm\) 0.41% for BRB, and 58.43 \(\pm\) 0.39% for MBR, which were higher than those of the corresponding DW extracts (Figure 2B). As shown in Figure 2C, the absorbance of FRAP at 593 nm in the 80% EtOH extracts of BR, BRB, and MBR at 1 mg/mL was 0.25, 0.23, and 0.15, respectively, which were higher than those in the corresponding DW extracts, with BR showing the highest antioxidant activity.  
Correlation Analysis Between Bioactive Compounds and Antioxidant Activities of 80% EtOH and DW Extracts of BR, BRB, and MBR  
To further analyze the correlation between the antioxidant activities of the 80% EtOH extracts of BR, BRB, and MBR and the contents of bioactive compounds, including \(\gamma\)-oryzanol, and \(\beta\)-glucan, we used Pearson correlation analysis and present the correlation coefficients in a heat map (Figure 3). The results reveal that apart from the total phenolic and flavonoid contents, which are primarily responsible for the antioxidant properties of the extracts, the \(\gamma\)-oryzanol content also shows a significantly positive correlation with the  
Figure 2: Antioxidant activities of 80% EtOH and DW extracts from BR, BRB, and MBR established based on (**A**) DPPH radical-scavenging activity; (**B**) ABTS radical-scavenging activity; and (**C**) ferric-reducing antioxidant power (FRAP) activity. All values are expressed as mean \(\pm\) standard deviation (SD). Different lowercase letters on the bars indicate statistical differences at \(p\) < 0.05.  
antioxidant activity of the 80% EtOH BR extract. A weak negative correlation was observed between the \(\beta\)-glucan content and the other variables.
## 4 Experimental Results and Discussion
### Convergence Analysis  
To verify the efficiency of the proposed method, a convergence analysis of the proposed method was conducted. Figure 7 shows the training performance on the Middleburry Stereo Dataset [30] and the testing of the performance on the image Dog. For evaluating the convergence of the modified CNN, the above-mentioned SAD was computed as a training error and Peak Signal to Noise Ratio (PSNR) was used as a testing error in each epoch. Here, an epoch denotes the training times.  
Figure 6: Right view of each stereoscopic pair in the testing dataset.  
It is evident in the figure that, with the increase in the epoch, SAD in training gradually decreases, whereas PSNR in testing progressively increases. Furthermore, the gradual convergence tendency with the increase in the epoch can be predicted. These results show that the reconstruction results are better with more training epochs until the modified CNN reaches stability. Furthermore, the performance of the proposed method surpasses that of the sparse coding (SC) method [13] baseline with a few training epochs, and it outperforms the SRCNN [15] with proper training epochs. Finally, it converges to the PSNR value of 46.66 dB on the image Dog.
## 2 Materials and Methods
### Test Setup and Instrumentation  
Figure 8 shows the experimental setup of the direct tensile test for the TRM coupons. The steel plates were utilized to clamp the gripping regions of the TRM coupons and fixed by steel bolts. The clamping force induced during fixing the steel plates may have caused a high compressive force to be acting on the gripping area of the TRM coupons. Therefore, to avoid any damage within the clamping areas, and to ensure an effective force transfer from the universal testing machine, carbon fiber reinforced polymer (CFRP) tabs with a thickness of 2 mm were utilized to strengthen the gripping regions of the TRM coupons (Figure 8a). The slippage phenomenon may have occurred at the interface between the CFRP tabs and the steel plates. Hence, the surface of the steel plates was roughened to increase the friction between them. Furthermore, the non-alignment of the TRM coupons and the steel plates may have triggered a bending moment during the tensile test. Thus, a steel bar which could rotate freely around a hinge (Figure 8a) was used to connect the TRM coupons with a testing machine. This clamping method was also used in the studies of [PERSON] et al. [39] and [PERSON] et al. [41]. To perform the tensile tests, a universal testing machine (UTM) with a loading capacity of 1000 kN was utilized. The tensile test was performed according to ASTM D3039, with the applied load under displacement control at 0.5 mm/min [42].  
To measure the tensile deformation, two linear variable displacement transformers (LVDT) were fixed at both sides along the longitudinal direction of the TRM coupons by means of the steel frames. Thus, the tensile strain of the TRM coupons was calculated as the average values obtained from LVDTs divided by the length of the central zone (180 mm).  
Figure 7: Photo of the test setup of TRM coupons subjected to sustained load.
## 2 Material and Methods
### Methods
#### 2.3.1 Estimation of WUE in the FPENC  
Utilizing the GEE platform, WUE was calculated as the ratio of GPP to ET resulting in 8-day intervals with a resolution of 500 m. The mean monthly WUE was derived from the average of the 8-day WUE values for each month. If two different months were represented in an 8-day cumulative period, the month with the majority of days received the assigned value. The mean annual WUE for each year was calculated similarly.  
Initially, we examined the spatial distribution and trends of annual mean WUE and related biophysical variables from 2003 to 2022. Vegetation types and land use categories were reclassified based on the MODIS land-cover map to select sampling locations across 10 different vegetation types (Figure 1). These locations were chosen to ensure that vegetation was present within a 500 m buffer. This approach facilitated the analysis of WUE variability in various vegetation groups in the FPENC at both monthly and annual intervals. The GPP, ET, and WUE values of each vegetation type were also computed for the expected littoral and swamp forests due to the lack of MODIS GPP and ET for their land use type.  
Long-term trends were assessed using the non-parametric Theil-Sen slope method [45]. In addition, the non-parametric Mann-Kendall test [46; 47] was employed to evaluate the significance of the identified trends in WUE. The Mann-Kendall test assumes that the data are serially independent. However, the times series of WUE may have autocorrelation, which might cause errors for the trend test, increasing the significance of data [44]. Therefore, the LOESS (STL) method was adopted to eliminate the influence of autocorrelation in the seasonal and trend decomposition of WUE in various vegetation types.  
Figure 2: A framework of the present study.
# Enhancing Underwater Images via Color Correction and Multiscale Fusion
## 3 Method of This Article
### Contrast Enhancement  
Guided filtering [30] is an image filtering technique used to smooth images while preserving details and edge information. Compared to traditional filtering methods, the guided filtering has better noise reduction and image smoothing capabilities while preserving edges.  
The principle of guided filtering is based on the following idea: it uses a guiding image, called the guidance image, to guide the filtering operation. The guidance image is typically an auxiliary image that is correlated with the input image, such as the grayscale image, gradient image, or other feature image derived from the original image. The guidance image provides guidance information about the image structure and features, allowing the filter to have a better understanding of the image content and edge information.  
HSV (Hue, Saturation, Value) is a commonly used color space that is more consistent with human perception when describing colors. The HSV color space decomposes colors into three components: hue, saturation, and value. Hue represents the basic attributes of a color, which is the name of the color we perceive, such as red, green, blue, etc. Saturation represents the purity or depth of a color. Colors with higher saturation have a bright appearance, while colors with lower saturation exhibit a darker or lighter effect. Value represents the brightness of a color.  
The HSV color space is used in image processing, where the V channel represents the image's brightness. This paper enhances the contrast of the image in the V channel by decomposing it into low- and high-frequency components using guided filtering. The low frequency preserves the main changes in illumination, while the high frequency contains the image details.  
\[V=V_{s}+V_{t} \tag{11}\]  
In Equation (11), \(V\) represents the brightness channel of the image, and \(V_{s}\) and \(V_{t}\) represent the low frequency and high frequency of the brightness channel, respectively.  
Foreground and background regions in underwater images exhibit distinct characteristics and require different contrast-enhancement techniques. In our approach, we first use a benign separation threshold strategy to segment the underwater image into foreground and background sub-images. Subsequently, we perform contrast enhancement on these pairs of sub-images and utilize the Laplacian Pyramid Gaussian Pyramid to fuse the two versions of contrast-enhanced images. This effectively achieves a smooth transition between the enhanced images and eliminates discontinuities between them.  
Through these steps, the visibility of underwater images is significantly improved while preserving the details and textures of the foreground and background areas.