esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion  
The pesticide entity-relation extraction technique is key for constructing large-scale pesticide knowledge graphs. Using this technique, we can further advance downstream applications such as knowledge discovery and pest control systems, which is of great significance for promoting sustainable agriculture, increasing yields, and preventing environmental pollution.  
Pesticide registration data are characterized by high information density, complex logical structures, large entity spans, and heterogeneous entity lengths. Existing joint extraction methods can be divided into multi-stage and single-stage approaches. Multi-stage joint extraction models show an interdependency between entity recognition and relation extraction results. Although they can handle issues such as nested entities and overlapping triplets, early-stage errors are often propagated to later stages, leading to exposure bias. Current single-stage joint extraction models extract entities and relations in parallel, resolving exposure bias affecting multi-stage methods. However, these models suffer from redundant computation when combining entities and relations. Furthermore, existing methods solely rely on semantic information for feature representation, making it difficult to capture the text's complex logical structures.  
To address pesticide registration data characteristics and exposure bias challenges, redundant computation, and insufficient representation capabilities in existing joint extraction methods, we propose a single-stage joint extraction model based on the HT-BES multi-dimensional labeling strategy: MD-SERel. Unlike existing research, we introduced a graph neural network into the encoding layer to extract syntactic structure information and fuse it with semantic information to enhance the model's feature representation capability. Additionally, for the multi-dimensional labeling framework layer, we designed an HT-BES labeling strategy, transforming the joint extraction task into a multi-label sequence labeling task. With the use of the scoring function and the fine-grained classification components, entities and relations are extracted in a single stage. The proposed MD-SERel model demonstrates advanced performance on the Pesticide Registration Dataset, balancing accuracy and computational efficiency.  
Although the model achieved strong performance, it still exhibited limitations in terms of capturing correlations between head and tail entities and their relationships. This may lead to label assignment errors, affecting the model's recall performance. To enhance the extraction capability, future studies could focus on optimizing the scoring function by introducing attention mechanisms. This would enable the more comprehensive exploration of correlations between head and tail entities and their relationships, further improving the model's overall extraction performance.
# Review of the AI-Based Analysis of Abdominal Organs from Routine CT Scans
## 2 Historical Evolution
### Deep Learning Revolution (2018-2024)
#### 2.3.10 Encoder-Decoder  
In 2020, [PERSON] et al. developed a method to automatically identify the central axial slice at the third lumbar vertebra (L3) vertebral level in CT scans and segment tissues including skeletal muscle (SM), subcutaneous adipose tissue (SAT), visceral adipose tissue (VAT), and intermuscular adipose tissue (IMAT). The approach involved an L3 slice localization network, trained on over 12,000 images, and a muscle-fat segmentation network based on an encoder-decoder CNN structure. Using CT datasets from cancer patients, the method achieved a slice localization error of \(0.87\pm 2.54\) on 1748 CT scans. For tissue segmentation, mean Jaccard scores were 97% for SM, 98% for SAT, 97% for VAT, and 83% for IMAT, evaluated on datasets of 1327 and 1202 test samples [21].
# Boosting Fraud Detection in Mobile Payment with Prior Knowledge
## 4 Machine Learning Model
### Extension to the Boosting Model by Incorporating Prior Knowledge  
In its standard form, the boosting model does not allow for the direct incorporation of prior knowledge. In this article, an extension to boosting algorithms is presented, which permits the incorporation of prior human knowledge as a means of compensating for training data shortage and improving the prediction result. For example, the typical GBDT model is an iterative decision tree algorithm, which consists of multiple decision trees, and no prior knowledge can be incorporated into this model as input or feedbacks.  
Figure 5 shows the flow chart of the extended boosting model. The following are 3 major modifications to the boosting model:  
First, during daily system operations, business experts analyze payment transactions and establish a database of prior human knowledge, including expert rules [50] and blacklists. The knowledge can be accumulated from historical fraud transactions or transferred from different domains [51].  
Second, prior human knowledge is applied to extract risk features, as shown in Section 3.3. Risk features together with normal features are input into the boosting model to perform training and testing.  
Third, the classification results are verified manually or automatically and are then fed back to prior knowledge database as new expert rule items and blacklist items, thus forming a continuous knowledge accumulation mechanism.  
In conclusion, prior human knowledge is incorporated into the boosting model, and we expect the extended model will be smarter in classification and more accurate in prediction. As typical boosting algorithms such as AdaBoost, GBDT and XGBoost are specified, the extended boosting model is entitled extended AdaBoost, extended GBDT or extended XGBoost.  
Figure 4: Boosting Machine Learning Model.
# A Posteriori Detection of Numerical Locking in \(hpq\)-Adaptive Finite Element Analysis
## 5 Effectivity of the Method in Model Problems
### Numerical Examples
#### 5.2.5 Generalizations  
Based on the above representative examples, and other analogous examples performed by the authors and not presented here due to their qualitative and quantitative similarity, one can state that:  
* The adaptation mode based on standard \(hp\)-adaptivity may fail when applied to the problems where the locking phenomena are present.
* The adaptation mode which allows modification of the initial mesh with the maximum possible longitudinal order of approximation may lead to underestimation of the final error value.
* The course of adaptation with the modification of the initial mesh by means of the optimized value of the longitudinal order of approximation is more effective in achieving the admissible error value than the previous two courses of adaptation.
## 2 Materials and Methods  
### Phantom Study  
This study used the CIRS dynamic thorax phantom (CIRS Inc., Norfolk, VA, USA) to validate the effect of using MF technology on target volume definition for cancer treatment planning. During 4D imaging, the target volume was set to 8 mL under all different respiratory motion situations, and [\({}^{18}\)F]Fluoro-2-deoxy-2-d-glucose ([\({}^{18}\)F]FDG) was injected at a concentration of 10 \(\mu\)Ci/mL. In addition to the static status, the movement pattern of the target (tumor) in the phantom included three pre-defined motions generated by the CIRS motion control software; namely, Sin, Cos[6], and Sharkfin. All motion patterns had the same settings: the superior-inferior (SI) direction had an amplitude of 5 mm (displacement = 10 mm), and the anterior-posterior (AP) and left-right (LR) directions were set to exhibit synchronized movement with an amplitude of 5 mm (displacement = 10 mm). Under the three motion patterns, 4D PET/CT and 3D PET/CT images wereobtained, and the T50% phase during which the tumor anatomical movement was smallest for the longest duration was used as the reference to build the MF-PET images. A threshold of 40% of the maximum standardized uptake value (SUVmax) was used to delineate the target volume. The differences in target volumes defined by PETMF, PET4D-T50%, PET3D, and PETStatic were investigated.
# Enhancing Underwater Images via Color Correction and Multiscale Fusion
## 3 Method of This Article
### Detail Enhancement  
The proposed method in this paper presents a simple approach for enhancing the details in underwater images. The texture details are adaptively amplified, and the blurred details are significantly improved using an enhancement coefficient K. Through extensive experiments and considering the influence of parameters on visibility and the human visual system, \(\lambda\) and \(\sigma\) in Equation (25) are set to be 8 and 1.  
\[\rm K=1+\lambda\exp\left(\frac{-\left|V_{t}(x,y)\right|}{\delta}\right) \tag{25}\]  
\(V_{t}(x,y)\) represents the pixel value of \(V_{t}\) at the pixel location (\(x\), \(y\)), while \(V_{et}(x,y)\) represents the pixel value of the texture layer at the pixel location (\(x\), \(y\)) after enhancement.  
Figure 3: (a) is a local contrast-enhancement image and (b) is a global contrast-enhancement image.  
\(\varepsilon\) is the parameter used to avoid the dense noise of enlarging the underwater image. In our experiment, \(\varepsilon\) is 0.005. Equation (26) is used to enhance the details.  
\[V_{et}(x,y)=\begin{cases}0&if|V_{t}(x,y)|<\varepsilon\\ \text{K}\times V_{t}(x,y)&else\end{cases} \tag{26}\]  
Finally, the enhanced input of the \(V\) channel is as follows:  
\[V_{final}=V_{es}+V_{et} \tag{27}\]  
The final step involves converting the enhanced \(V\) channel image and the H, S channel image from the HSV color space to the RGB color space to obtain the improved underwater image.
# A Review of Double-Walled and Triple-Walled Carbon Nanotube Synthesis and Applications
## 5 Applications Involving DWNTs and TWNTs
(**b**) The current density is plotted against voltage for several samples prepared in different ways: Sample A1 (SiNW/DWNT heterojunction without redox electrolyte), Sample A2 (SiNW/DWNT heterojunction with redox electrolyte) and Sample B2 (Si wafer/DWNT heterojunction with redox electrolyte). The inset brings the efficiency obtained for each sample. The redox electrolyte improves the efficiency in SiNW/DWNT-based devices, but worsens the efficiency in Si wafer/DWNT-based devices. Reproduced with permission from [188], Copyright American Chemical Society, 2009.  
more [189; 190; 191; 192; 193; 194; 195; 196; 197]. As an example, p-aminophenol-modified multi-walled carbon nanotubes have been successfully tested as a sensor to detect vitamin C (ascorbic acid) via its electrochemical oxidation [197]. Furthermore, MWNTs/Pd-Ir composites have also been effectively utilized as supramolecular imprinted sensors capable of detecting carbofuran [194], and electrochemical sensors for detecting chloramphenicol based on multi-walled tubes were designed and successfully tested, as well [189]. Sensors for controlling and monitoring of human motions were also recently designed [190]. It is also important to comment that the surfaces of the CNTs constituting a MWNT system, as well as the interstitial site present in bundled samples will change with increasing the number of layers in the MWNT system. As an example, the use of DWNTs with appropriate diameter distributions has been used for H\({}_{2}\) adsorption [198]. The DWNT systems exhibited a H\({}_{2}\) adsorption two-times higher than the adsorption observed in SWNT systems [198]. In fact, as the number of walls increases, the molecular potential observed at the constituent carbon nanotube surfaces increases, but on the other hand, the pore size, the pore volume and the surface area decrease. Therefore, there should be an ideal number of walls and diameters in MWNT systems, so that different molecules can be more efficiently adsorbed. These are just a few examples of how industry can take advantage of the electrical, vibrational and mechanical properties of the multi-walled carbon nanotubes, which include DWNTs and TWNTs.
# Novel Scoring for Energy-Efficient Routing in Multi-Sensored Networks
## 3 The Proposed Mechanism
### Preliminary Formation for the Proposed Work
#### 3.1.1. Hop Level  
The networks are divided into a hierarchical format of interconnected nodes. The intermediate nodes with a direct connection to a sink are denoted as having a hop level of one, while the nodes at the end boundaries have the maximum level values. Each node keeps its own hop level to determine its distance from the sink. The hop level can be used to define the number of nodes in a route towards the sink. Equation (1) shows the hop level for a node within a network of \(n\) nodes, as follows:  
\[1\leq\ HL_{i}\leq n \tag{1}\]
## 5 Conclusions  
A special technique of microstructural analysis (STMA) that utilizes advantages of GIS technologies is presented. Applying the GIS approach simplifies detection, vectorization and processing of large amounts of microstructural data (one thin section can contain up to \(10^{5}\) cracks). The GIS tools of STMA makes it possible to link all collected samples and their detected characteristics to maps of different types, as well as to simultaneously analyze microstructures and macrostructures identified via topographic and tectonic map data and satellite images. There is a unique possibility of linking and upscaling macro-, meso- and micro-characteristics of a rock massif, as well as restoring the sequence of changes in tectonic stress-strain fields at different scale levels and times of origination.  
Applying the STMA algorithm, the structure of the damage zone of the Primorsky Fault was detected. Three permeable zones that are consistent with the results of petrographic studies have been identified. The thickness of the zone of most intensive deformations is estimated as 10-20 m.  
Figure 13: Scheme for interpreting the orientations of the principal axes of stress fields. (**A**) Interpretation of orientations of the main axes of the stress field performed for single rock samples based on the analysis of rose diagrams of microfractures, taking into account the fact that most studied microstructures are cracks of mode I; (**B**) interpretation of the orientations of principal axes of the modern stress field, performed via the analysis of lineaments in the area of the Sarma River [75; 101; 102]. 1—sampling points and their numbers; 2—Primorsky Fault; 3—cracks of mode I; 4—pair cracks of mode II; 5—orientation of the main axes of compression (red) and tension (blue).  
**Author Contributions:** All authors contributed to the study conception and design. Conceptualization, S.U. and A.O.; methodology, S.U., A.S. and I.N.; software, I.N., A.U. and G.G.; validation, S.U., A.O., I.N. and A.S.; formal analysis, S.U., A.U. and A.G.; investigation, S.U., A.O. and A.G.; resources, S.U. and A.O.; data curation, G.G. and A.O.; writing--original draft preparation, S.U. and A.O.; visualization, S.U. and A.S.; supervision, S.U.; project administration, A.O.; funding acquisition, A.O. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Russian Science Foundation, Grant No. 20-77-10087.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** All the data presented in this study are available from corresponding author on request.  
**Acknowledgments:** Rock samples for analyses were delivered by IDG RAS in the frames of the Program No. FMWN-2022-0020 of the Ministry of Science and Higher Education of Russian Federation. The development of the special technique of microstructural analysis (STMA) was carried out by IGEM RAS in the frames of the Program of the Ministry of Science and Higher Education of Russian Federation.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Method
### Autoregressive Integrated Moving Average (ARIMA) Model
#### 2.6.1 Identification  
The first step of the ARIMA model is to estimate the stationarity of the time series, which can be examined by the autocorrelation function and partial autocorrelation function. The attenuation rate of the autocorrelation function plot reflects whether the data are stable or not. Non-stationary time series data sets require a different process to make them stationary. The ARIMA model can be defined by three operators (\(p\), \(d\), \(q\)), where \(p\), \(d\), \(q\) denotes the orders of the auto regressive (AR) process, the differencing process, and the moving average (MA) process, respectively, and is given by:  
\[\varnothing_{p}(B)(1-B)^{d}X_{t}=\theta_{q}(B)\varepsilon_{t} \tag{9}\]  
where \(X_{t}\) is the GPP at time point \(t\); \(X_{t}\) is the backshift operator that follows \(BX_{t}=X_{t-1}\), \(B^{2}X_{t}=BBX_{t}=BX_{t-1}=X_{t-2}\),..., \(B^{k}X_{t}=X_{t-k}\); \(\varnothing_{p}(B)\) is the AR operator and \(\varnothing_{p}(B)=1-\varnothing_{1}B-\varnothing_{2}B^{2}-\ldots-\varnothing_ {p}B^{p}\); \(\theta_{q}(B)\) is the MA operator and \(\theta_{q}(B)=1+\theta_{1}B+\theta_{2}B^{2}+\ldots+\theta_{q}B^{q}\); and \(\varepsilon_{t}\) represents the random errors that resemble a white noise process \(\varepsilon_{t}\sim WN(0,\sigma^{2})\).
# Pathological Gait Classification Using Early and Late Fusion of Foot Pressure and Skeleton Data
## 2 Related Works
### Multimodal-Based Approaches  
In [35], an automated knee osteoarthritis (KOA) classification algorithm based on the Kellgren-Lawrence (KL) grading system is examined utilizing radiographic imaging and gait analysis data. The model achieves an F1-score, sensitivity, and precision of 0.70, 0.76, and 0.71, respectively.  
In [16], a DL-based multimodal model is presented for classifying six gait types, including one normal and five pathological (antalgic, lurch, steppage, stiff-legged, and Trendelenburg) gaits. This model uses both skeletal and foot pressure data. To efficiently extract features from these two different data types for classification, the model inputs sequential skeletal data into recurrent neural network (RNN)-based encoding layers and average foot pressure data into a CNN. The classification accuracy for the pressure-based and skeleton-based single models is 68.82% and 93.40%, respectively. However, the hybrid model outperforms them with an accuracy of 95.66%. Subsequently, the model underwent improvement, achieving an accuracy of 97.60% through a three-step training process.  
In [36], gait freezing episodes were classified with an accuracy of 98.1% through the data fusion of scalograms from Wi-Fi sensing and spectrograms from radar sensors. Additionally, ref. [37] explores sensor fusion using data from ambulatory inertial sensors (AISs) and plastic optical fiber-based floor sensors (FSs). The model attains an accuracy of 88% with ANN and 91% with CNN, as it learns optimal data representations from both sensor modalities.
# Adaptive Impedance-Based Fault Location Algorithm for Active Distribution Networks
## 2 Proposed Generalized Fault Location Equations
### Ground Faults  
Consider the generalized model of a fault illustrated in Figure 2. By modifying the states of the switches \(s1,\ s2,\ s3\), and maintaining switch \(s4\) closed, any phase-to-ground fault type is obtained.  
Using this model it is possible to derive [11]:  
\[\left[\begin{array}{c}V_{F_{k}}\\ V_{F_{b}}\\ V_{F_{c}}\end{array}\right]=\left[\begin{array}{ccc}Z_{F_{x}}+Z_{F_{g}}&Z_{F_{ g}}&Z_{F_{g}}\\ Z_{F_{g}}&Z_{F_{g}}+Z_{F_{g}}&Z_{F_{g}}\\ Z_{F_{g}}&Z_{F_{g}}&Z_{F_{g}}+Z_{F_{g}}\end{array}\right]\cdot\left[\begin{array} []{c}I_{F_{k}}\\ I_{F_{b}}\\ I_{F_{c}}\end{array}\right] \tag{1}\]  
In Equation (1), only the faulty phases have fault currents that are different from zero. From Figure 2, an expression for the fault point voltage is obtained, depending on the fault distance.  
\[\mathbf{V}_{F}=\mathbf{d}_{x}\cdot\mathbf{V}_{k}-\mathbf{b}_{x}\cdot\mathbf{I}_{k,F} \tag{2}\]  
Figure 1: Power distribution system model with a fault between nodes \(k\) and \(k+1\). DER: distributed energy resources.  
Figure 2: Distribution line model for different types of fault.  
where,  
\[\mathbf{d}_{x}=\mathbf{I}+0.5\cdot x^{2}\cdot\mathbf{Z}_{abc}\cdot\mathbf{Y}_{abc} \tag{3}\]  
\[\mathbf{b}_{x}=x\cdot\mathbf{Z}_{abc} \tag{4}\]  
Replacing Equation (2) in Equation (1), splitting into its real and imaginary parts, and considering \(Z_{F_{w}}\) and \(Z_{F_{g}}\) as pure resistances, a generalized expression for phase-to-ground faults is obtained, as shown by Equation (5).  
\[x^{2}\cdot\left[0.5\cdot\sum_{w\in\Omega_{w}}\Im\big{\{}M_{w}\cdot I_{F_{w}}^{ *}\big{\}}\right]-x\cdot\left[\sum_{w\in\Omega_{w}}\Im\big{\{}N_{w}\cdot I_{F_{ w}}^{*}\big{\}}\right]+\left[\sum_{w\in\Omega_{w}}\Im\big{\{}V_{k_{w}}\cdot I_{F_{w}}^{* }\big{\}}\right]=0 \tag{5}\]  
The variables \(M\) and \(N\) are defined as:  
\[\left[\begin{array}{c}M_{a}\\ M_{b}\\ M_{c}\end{array}\right]=\left[\begin{array}{cccc}Z_{aa}&Z_{ab}&Z_{ac}\\ Z_{ba}&Z_{bb}&Z_{bc}\\ Z_{ca}&Z_{cb}&Z_{cc}\end{array}\right]\cdot\left[\begin{array}{cccc}Y_{aa} &Y_{ab}&Y_{ac}\\ Y_{ba}&Y_{bb}&Y_{bc}\\ Y_{ca}&Y_{cb}&Y_{cc}\end{array}\right]\cdot\left[\begin{array}{c}V_{k_{a}}\\ V_{k_{b}}\\ V_{k_{c}}\end{array}\right] \tag{6}\]  
\[\left[\begin{array}{c}N_{a}\\ N_{b}\\ N_{c}\end{array}\right]=\left[\begin{array}{cccc}Z_{aa}&Z_{ab}&Z_{ac}\\ Z_{ba}&Z_{bb}&Z_{bc}\\ Z_{ca}&Z_{cb}&Z_{cc}\end{array}\right]\cdot\left[\begin{array}{c}I_{k_{F_{ a}}}\\ I_{k_{F_{b}}}\\ I_{k_{F_{c}}}\end{array}\right] \tag{7}\]
# GRACE/GFO and Swarm Observation Analysis of the 2023-2024 Extreme Drought in the Amazon River Basin
## 3 Data and Methods
### Method
#### 3.2.5. Drought Characteristics  
In this study, we based the definition of drought on GRACE-DSI, that is, a drought is considered to have occurred in a region if the monthly GRACE-DSI is less than \(-0.5\)[31]. Drought assessment indicators include duration, drought severity, peak, and drought area ratio (DAR) [17]. Duration denotes the total number of months of a drought, peak denotes the maximum GRACE-DSI during the drought, and DAR denotes the ratio of area subject to drought to total area. Depending on the DAR, the drought can be divided into four categories, as shown in Table 3[54; 55].  
The expression of drought severity is as follows [56]:  
\[S=\overline{D}\times M \tag{18}\]  
where \(S\), \(\overline{D}\), and \(M\) denote the drought severity, the average GRACE-DSI during the drought, and the drought duration up to the calculation month, respectively