esa-sceva/satcom-chunk-collection huggingface dataset:
# AVO-Friendly Velocity Analysis Based on the High-Resolution PCA-Weighted Semblance
## 4 Conclusions  
In this study, we present a new weighted semblance spectrum to improve the resolution of the NMO-based velocity analysis. The proposed method utilizes principal component analysis to measure the spatial coherence or low-rank property of the NMO-corrected data, and construct a new weighting function through the ratio between principal components (eigenvalues). The new weighting function was highly sensitive to slight changes of seismic events when we used different scanning velocities to apply NMO correction, so our method has higher resolution than the conventional method. In addition, the proposed method still performed well for the AVO anomalies, and the energy clusters of new semblance spectrum were also strong in the AVO region, which is of great benefit in avoiding the multiplicity of the auto-picking algorithm. Numerical experiments on the synthetic and field data sets also proved that the new method has a better performance in velocity analysis and provides more accurate picked velocities than those of conventional methods.  
Conceptualization, C.Z. and G.C.; methodology, C.Z. and G.C.; software, G.C.; validation, L.F.; investigation, L.F. and J.L.; writing--original draft preparation, C.Z. and G.C.; writing--review and editing, J.L.; project administration, C.Z.; funding acquisition, C.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Prospective and Basic Research Project of CNPC (2021D)[0503], the Strategic Priority Research Program of the Chinese Academy of Sciences (XDA14010403), the National Natural Science Foundation of China (No. 42172145), and the China National Science and Technology Major Project (2016 ZX05007-002).  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.
## 3 Results
### Flood Evolution Simulation Analysis
The flood depth and flow velocity data from the eight monitoring points are presented in Figure 6. Then, 45 min after the dam breach, all monitoring points detected the presence of water flow, with the flood reaching its peak at 75 min. Monitoring Point 1 (Shanmei Reservoir Management Office) is located at a sharp U-shaped bend in the downstream river channel. Due to the effects of vortex circulation and flood fluctuations, the flow velocity at this point can reach up to 22.18 m/s. Monitoring Point 7 (Xiawei Village) is positioned just before a T-shaped river bifurcation downstream, where the flow exhibits complex behavior, characterized by high kinetic energy at the front. The nearby riverbank experiences a water blockage, leading to a rise in water levels. The maximum flood depth reaches 18.65 m. Monitoring Points 3 (Sanluo Village), 4 (Nan'an Wancheng Paper Industry Co., Ltd., Nanan, China), 5 (Shinan Village), and 8 (Shikou Village) are located near the riverbank at lower elevations. This results in similar characteristics for flood depth and velocity. In contrast, Monitoring Points 2 (Nan'an Yanjiang Cement Co., Ltd., Nanan, China) and 6 (Jun Village), situated at relatively higher elevations, are less impacted by the flood, with the maximum water level not exceeding 3.45 m.  
Figure 5: Flood evolution distribution map. (**a**) Flood depth map at 15 min; (**b**) flood depth map at 45 min; (**c**) flood depth map at 75 min; (**d**) flow velocity map at 15 min; (**e**) flow velocity map at 45 min; (**f**) flow velocity map at 75 min.
## 4 Discussion  
### Residual Analysis and Error Distribution for Model Performance Evaluation  
Figure 8 compares the results of different models using several graphs. Residual plots show the differences between the actual and predicted values, which help assess accuracy and bias. Ideally, the residuals should be evenly spread around zero, indicating no bias in the model's predictions. Models like GP and XGBoost perform well, with residuals mostly close to zero, showing good accuracy. On the other hand, scattered residuals highlighted areas where predictions were less accurate. These graphs also reveal the range of errors, with narrower distributions around zero indicating better performance, as seen in GP and XGBoost. Wider or skewed distributions suggest models with lower accuracy or potential biases.  
The scatter matrix in Figure 8 compares actual \(C_{c}\) values with the predicted ones for each model. Points closer to the diagonal line indicate more accurate predictions. GP and XGBoost models show tight clustering along the diagonal direction, reflecting higher accuracy, while scattered points in other models reveal more errors. This visualization is useful for identifying how well each model generalizes to new data and whether there are any consistent issues. It offers a clear view of the models' strengths and weaknesses, making it easier to understand their overall performance.
## 2 Proposed Solution
### Graph Representation  
The information extracted using different models in Sections 2.1-2.4 need to be combined to provide meaningful insights into the threat level of the given scene. This is accomplished by encoding the data into a graph structure. This section describes how the graph structure is modeled using the different outputs from the models for interpretation.  
The information retrieved from the video is stored as a time-varying graph \(G(t)\) given by  
\[G(t) =\big{(}V(t),E(t)\big{)} \tag{11}\] \[\text{and}\] \[V(t) =\{v_{1}(t),v_{2}(t),\ldots,v_{n}(t)\}\] (12) \[E(t) =\{e_{1,1}(t),e_{1,2}(t),\ldots,e_{ij}(t),\ldots,e_{n,n}(t)\} \tag{13}\]where \(V(t)\) is the set of vertices and \(E(t)\) is the set of edges at time \(t\). Each person \(P_{i}\) is denoted by a vertex \(v_{i}(t)\) which contains the features representing the person extracted from the video as time-varying vertex parameters. The vertex \(v_{i}(t)\) is given by  
\[v_{i}(t)=\left[\mathit{location}_{i}(t),\mathit{mask}_{i}(t),\mathit{group}_{ i}(t)\right] \tag{14}\]  
where \(\mathit{location}_{i}(t)=(x_{i}(t),y_{i}(t))\) is a two-tuple that represents the position of the person \(P_{i}\) at time \(t\) obtained through perspective transform to a bird's-eye view position on a 2D plane (refer to Section 2.2). \(\mathit{mask}_{i}(t)=c_{m}\) is two-tuple, which shows the confidence level that a person \(P_{i}\) is wearing a mask at time \(t\). This information is extracted from \(bb_{mi}(t)\) depending on the index \(ID_{mi}(t)\) (refer to Section 2.4). \(\mathit{group}_{i}(t)\) is a matrix that represents the probability that two people belong to the same group (refer to Section 2.3). The edge \(e_{ij}(t)\) is a binary value (\(0/1\)) that represents the presence (denoted by \(1\)) or absence (denoted by \(0\)) of an interaction between person \(P_{i}\) and \(P_{i}\) at time \(t\) detected using [39]. \(E(t)\) is stored as a sparsely filled adjacency matrix with null values for instances where interactions are not detected. A visual example of a frame and its constructed graph is shown in Figure 4.
## 2 Materials and Methods
### Machine Learning Algorithms Used in the Study
#### 2.4.3 Gradient Boosting Regression  
Gradient boosting regression (GBR) refers to an ensemble-trained supervised machine learning model. Boosting is a technique that involves combining many simple models into a unified composite model. Boosting is frequently described as an additive model because basic models are sequentially added while the trees of the model remain unchanged. Combining more fundamental models results in more accurate predictions. Gradient boosting aims to reduce losses via gradient descent, a first-order iterative optimization procedure. Employing a squared error loss function, decision trees act as the weak learners within the framework of gradient boosting. GBR proceeds by training a weak model to map features to the predicted residuals of the weak model. These residuals are incorporated into the input of the current model, guiding it toward the intended outcome. When this process is repeated on a regular basis, the model's overall predicted accuracy steadily improves [87; 88].
# Fabrication of Eco-Friendly Graphene Nanoplatelet Electrode for Electropolishing and Its Properties
###### Abstract  
Electropolishing is one of the most widely applied metal polishing techniques for passivating and deburring metal parts. Copper is often used as cathode electrode for electropolishing due to its low electrical resistance and low flow values. However, during the electropolishing process, elution of the cathode electrode caused by the electrolyte and remaining oxygen gas also causes critical water pollution and inhibits electropolishing efficiency. Therefore, to achieve an efficient and eco-friendly electropolishing process, development of a highly corrosion resistive and conductive electrode is necessary. We developed a highly oriented graphene nanoplatelet (GNP) electrode that minimizes water pollution in the electropolishing process. We functionalized GNP by a one-step mass-productive ball-milling process and non-covalent melamine functionalization. Melamine is an effective amphiphilic molecule that enhances dispersibility and nematic liquid crystal phase transformation of GNP. The functionalization mechanism and the material interaction were confirmed by Raman spectroscopy after high-speed shear printing. After the electropolishing process by melamine-functionalized GNP electrodes, 304 stainless steel samples were noticeably polished as copper electrodes and elution of carbon was over 50 times less than was the case when using copper electrodes. This electropolishing performance of a highly oriented GNP electrode indicates that melamine-functionalized GNP has great potential for eco-friendly electropolishing applications.  
graphene; electrode; functionalization; electropolishing; eco-friendly +
Footnote †: journal: Applied Sci. 2021
## 2 Materials and Method
### Model Accuracy Evaluation  
Three indices were used to evaluate the performance of the model. (1) The coefficient of determination (R\({}^{2}\)), which describes to what extent the variation in the observations can be explained by the models. R\({}^{2}\) ranges from 0 to 1, where a value near 1 indicates a good performance of the model. (2) The root mean square error (RMSE) represents the sample standard deviation of the differences between the predicted values and observed values. A smaller RSME indicates a better performance of the model. (3) The Nash-Sutcliffe model efficiency coefficient (NSE) is often used to assess the power of a model. The coefficient ranges from \(\
eg\) to 1. A NSE close to 1 indicates a better match between the modeled and observed values. The formulas for R\({}^{2}\), RMSE and NSE are as follows:  
\[R^{2}= 1-\frac{\sum\limits_{t=1}^{t=n}(M_{\mathrm{t}}-O_{\mathrm{t}})^{2}}{ \sum\limits_{t=1}^{t=n}(O_{\mathrm{t}})^{2}} \tag{12}\] \[RMSE= \sqrt{\frac{1}{n}\sum\limits_{t=1}^{t=n}(M_{\mathrm{t}}-O_{ \mathrm{t}})^{2}} \tag{13}\]
## 1 Introduction
Compared with the previous research in this field, the paper has three novel contributions: (i) the differential coherent method is used to remove the effect of frequency deviation, and the differential delay time is set to be a multiple of bit cycle to remove the influence of NH code; (ii) the maximum likelihood function detection is used to improve the detection probability of weak signals; (iii) Monte Carlo simulations and real data tests are carried out to verify the reliability and performance improvement of the proposed algorithm. The rest of this paper is organized as follows: Section 2 describes the BeiDoi signal model and the conventional bit synchronization method. Section 3 gives detailed descriptions of the proposed algorithm. Section 4 presents the results and analysis of the Monte Carlo simulations. Finally, Section 5 is the conclusion.
# Improving Sentence Representations via Component Focusing
###### Abstract  
The efficiency of natural language processing (NLP) tasks, such as text classification and information retrieval, can be significantly improved with proper sentence representations. Neural networks such as convolutional neural network (CNN) and recurrent neural network (RNN) are gradually applied to learn the representations of sentences and are suitable for processing sequences. Recently, bidirectional encoder representations from transformers (BERT) has attracted much attention because it achieves state-of-the-art performance on various NLP tasks. However, these standard models do not adequately address a general linguistic fact, that is, different sentence components serve diverse roles in the meaning of a sentence. In general, the subject, predicate, and object serve the most crucial roles as they represent the primary meaning of a sentence. Additionally, words in a sentence are also related to each other by syntactic relations. To emphasize on these issues, we propose a sentence representation model, a modification of the pre-trained bidirectional encoder representations from transformers (BERT) network via component focusing (CF-BERT). The sentence representation consists of a basic part which refers to the complete sentence, and a component-enhanced part, which focuses on subject, predicate, object, and their relations. For the best performance, a weight factor is introduced to adjust the ratio of both parts. We evaluate CF-BERT on two different tasks: semantic textual similarity and entailment classification. Results show that CF-BERT yields a significant performance gain compared to other sentence representation methods.  
natural language processing; sentence representation; sentence embedding; component focusing; semantic textual similarity +
Footnote †: journal: Applied  
Article Improving Sentence Representations via Component Focusing [PERSON] _et al._ (2020); 10, 958; doi:10.3390/app10030958
## 2 Materials and Methods
### Surface Quality Measurements  
Surface characterization was carried out using the MarSurf XT20 system from MAHR Gottingen GmbH (Gottingen, Germany), equipped with an MFW 250 scanning head (Figure 3a). The instrument features a \(\pm\)750 \(\upmu\)m range tracing arm and a stylus tip with a 2 \(\upmu\)m radius and a 90\({}^{\circ}\) tip angle. The measurements were conducted at a scanning speed of 0.5 mm/s, with a contact force of 0.7 mN and a lateral resolution of 5 \(\upmu\)m. For each sample and CNC cutting parameter combination, four surface profiles of 50 mm were recorded (Figure 3b), all measured perpendicular to the grain direction. For the shaved surfaces, the procedure was the same, but the number of measured profiles was increased to six per sample.  
The measured surface profiles were processed using MARWIN XR20 software (version 9.00-23 from 2013) supplied by the instrument's manufacturer. The initial step involved the removal of form error through polynomial curve fitting, which eliminated large-scale geometric deviations. This correction enabled the extraction of the primary profile by subtracting the fitted curve from the raw data, following [21]. The primary profile retained both waviness and roughness components, which were distinguished based on wavelength, with longer wavelengths corresponding to waviness, and shorter wavelengths to surface roughness.  
To separate the roughness profile from longer wavelength irregularities, a robust Gaussian regression filter was applied following [22], using a 2.5 mm cut-off length, as recommended for wood surface analysis [23; 24]. From the filtered roughness profiles, roughness parameters were calculated [21], such as the arithmetical mean deviation (Ra) and the maximum profile valley depth (Rv). Given the hierarchical and anisotropic nature of wood, Abbott-Firestone curve parameters were also included to characterize the functional aspects of the surface. These comprised Rk (core roughness depth), Rpk (reduced  
Figure 3: Experimental setup for roughness measurement: (**a**) Marsurf XT20 system; (**b**) measuring the sample surface across the grain.  
peak height), and Rvk (reduced valley depth) [21]. These parameters are particularly relevant in wood surface evaluation, as supported by prior studies [24; 25; 26], with further methodological details provided in [27]. The use of these three descriptors is essential to capture the complex morphology of wood surfaces and to provide a comprehensive understanding of their topographical characteristics. The significance of the results for all processing parameters combinations was tested with single-factor and two-factor ANOVA, without replication for \(p<0.05\). Individual primary and roughness profiles were further obtained in MathCAD Professional 2000 (MathSoft Inc., Cambridge, MA, USA) by processing the original measured ASCII data to allow for comparisons between tools and cutting parameters.
## 4 Discussion of the Results
### Definition of Comparison Parameters
The only exception was represented by F6-M2-PVA samples, possibly because the addition of short fibres in a matrix embedding a narrow-grid textile introduced a defect at the fabric-matrix interface, favouring internal slippage. In addition, it is important to stress that the higher porosity of matrix M2 entailed a lower fibre pull-out strength; moreover, the increment of \(EF_{TRC}\) was higher in the cases with low-grammage textiles (F1 and F2) because, given the fixed volume fraction of short fibres, the percentage increase of the reinforcement ratio was higher. * The composites manufactured with epoxy-impregnated fabrics (F2-F4) were more efficient, probably due to a greater chemo-mechanical bond (greater surface roughness combined with stiffer nodal connections between warp and weft yarns, as discussed in Section 4.1). Moreover, the two composite efficiency factors (\(EF_{TRC}\) and \(EF_{TRC_{f}}\)) were closer, due to the greater efficiency of the plain fabric (see the warp \(EF_{f}\) values of Table 5). Figure 13 reveals the effect of short fibres addition on the ductility/energy absorption parameters. Normalising each average value with respect to the corresponding plain TRC one, was noticed that:  
* In the case of low to medium grammage fabrics (F1 and F2), a general increase of the average stress \(\sigma_{I\_II}\) was observed. Moreover, greater control over damage development was achieved. An overall increase of the total absorbed energy was observed, in particular in M1-based composites, confirming preliminary considerations. * In the case of heavy-duty fabrics (F6 and F7, already characterised by a great reinforcement volume fraction), the responses became stiffer and showed a reduction of \(\epsilon_{2}\) and \(U_{T\_I\_II}\); consequently, the improvement of the mechanical capacity could only be assessed through the efficiency factors. Figure 12: Efficiency factors in uniaxial tension for M1-based (**a**) and M2-based (**b**) TRC composites. Figure 13: Ductility/energy absorption parameters in uniaxial tension for M1-based (**a**) and M2-based (**b**) TRC composites: effect of fibres addition.
## 3 Results
### Compared with Other Methods
#### 3.2.3 The Number of Matching Feature Points  
In order to verify that the algorithm in this article can retain more accurate matching points, this experiment tested the number of feature point matchings for various algorithms under easy scenarios (\(V201\)) and hard scenarios (\(V203\)), as shown in Table 4. In easy scenarios with less interference, each algorithm can match more feature points, and tracking 260 feature points can match 220 to 240. However, in hard scenes with blurring caused by dithering and large weak texture regions (\(V203\)), the number of feature points matched by various algorithms decreases sharply, and RANSAC only matches 134 feature points, our proposed method can still match 164 feature points. Figure 3 shows the feature points that RANSAC and our proposed method. As described in Section 2.4, RANSAC removes some correctly matched points, which has little impact in scenarios with more tracking feature points such as \(V201\). However, this can have a significant negative impact when the image is blurred, the quality is not high, and the number of feature points tracked is small. As shows in Figure 3a, the number of feature points matched to some images is even less than 20, which can lead to reduced constraints between images, thereby affecting the accurate estimation of posture. Figure 3b shows that our proposed method matches more feature points than RANSAC, ensuring sufficient feature points to provide pose constraints even when the image quality is poor.
# Wound Dressing Materials: Bridging Material Science and Clinical Practice
## 4 Antimicrobial Dressings
### Chitosan-Based Dressings  
Chitosan, a natural biopolymer derived from chitin found in the exoskeletons of arthropods and insects, has gained significant attention for its broad-spectrum antimicrobial activity against both bacteria and fungi. This polysaccharide exhibits various mechanisms of action, which can vary depending on the microorganism and the chemical properties of the chitosan. High-molecular-weight chitosan, due to its larger size, typically acts extracellularly, unable to permeate the cell membrane or wall. Its antimicrobial effects in this case are believed to result from its ability to chelate essential metals, disrupt nutrient transport, and alter the integrity of microbial membranes, preventing the microorganisms from thriving and reproducing. In contrast, low-molecular-weight chitosan, which is smaller in size, has the ability to penetrate cell membranes, enabling it to target intracellular processes. Once inside the cell, low-molecular-weight chitosan can interact with DNA, RNA, and mitochondrial activity, interfering with cellular functions critical to the microorganism's survival. This dual mechanism of action--both extracellular and intracellular--makes chitosan a versatile antimicrobial agent. Its unique properties not only provide effective microbial control but also make it a valuable candidate in various biomedical applications, such as wound healing, drug delivery, and infection prevention [109]. Chitosan's antimicrobial and biocompatible properties make it suitable for incorporation into various wound dressings. These include hydrogels for maintaining a moist environment, breathable films, absorbent sponges for wound cavities, nanofibers mimicking the extracellular matrix, high-exudate-absorbing foams, particles and beads for therapeutic delivery, protective membranes, and composite dressings that combine chitosan with other materials for enhanced wound care [109]. Despite its versatility, chitosan's antimicrobial efficacy is generally lower than that of conventional antimicrobial agents. Therefore, additional research is needed to better understand its potential and optimize its use in antimicrobial wound dressings.