esa-sceva/satcom-chunk-collection huggingface dataset:
# Borehole Trajectory Optimization Design Based on the SAC Algorithm with a Self-Attention Mechanism
## 1 Introduction
It also has geological adaptability, guiding the trajectory through high-quality reservoirs, bypassing unfavorable areas such as high-pressure layers and easily collapsed strata. Finally, the construction feasibility is fully considered, and the engineering constraints such as the slope limit, tool performance, and measurement accuracy are fully considered. By reasonably allocating the weight of each objective, the collaborative optimization and organic unification of these important objectives are realized. In summary, this study constructs a new method of intelligent wellbore trajectory optimization design for a complex geological environment by integrating a self-attention enhanced SAC algorithm, WGAN geological modeling technology, and a multi-objective reward system. The research results not only significantly improve the intelligence level and optimization performance of trajectory design in dynamic and complex environments, but also provide new theoretical support and technical path for safer, more efficient, and more accurate geosteering drilling in oil and gas drilling engineering. While deep reinforcement learning (DRL) has shown promise in trajectory optimization tasks, most existing approaches overlook fine-grained geological heterogeneity and lack spatial perception capabilities. Moreover, the integration between attention mechanisms and SAC-based drilling agents remains underexplored. This study addresses these gaps by designing an attention-enhanced SAC model tailored for adaptive borehole path planning under complex geological constraints. The remainder of this paper is organized as follows: Section 2 introduces the SAC algorithm and its integration with the self-attention mechanism. Section 3 details the reinforcement learning environment, including geological data generation, state-action design, and reward system construction. Section 4 presents experimental design,result analysis under multiple obstacle scenarios, and a comparative study of the proposed method. Finally, Section 5 summarizes the key findings and outlines the potential for future research.
## 3 Results and Discussion
### Chromatography and Mass Spectrometry of ISP Extracts
\begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{2}{*}{**Sample**} & \multicolumn{5}{c}{**Relative Area of Peaks (\%) 1**} \\ \cline{2-7}  & _m/z Range_ & _m/z Range_ & _m/z Range_ & _m/z Range_ & _m/z Range_ & _m/z Range_ \\  & **0.5–2 kDa** & **2–4 kDa** & **4–6 kDa** & **6–8 kDa** & **8–10 kDa** & **>10 kDa** \\ \hline Meat, 2 \({}^{\circ}\)C & 14.99 & 47.31 & 22.29 & 9.68 & 3.47 & 2.26 \\ Meat, 7 \({}^{\circ}\)C & 11.95 & 43.71 & 26.21 & 12.03 & 3.74 & 2.36 \\ Meat, 12 \({}^{\circ}\)C & 14.08 & 45.40 & 23.01 & 11.27 & 3.59 & 2.64 \\ \hline Brine, 2 \({}^{\circ}\)C & 14.95 & 44.64 & 21.11 & 9.05 & 5.89 & 4.36 \\ Brine, 7 \({}^{\circ}\)C & 14.57 & 45.83 & 18.04 & 8.28 & 5.92 & 7.36 \\ Brine, 12 \({}^{\circ}\)C & 18.26 & 60.88 & 14.49 & 4.48 & 1.31 & 0.59 \\ \hline All meat extracts 2,3 & 13.68 \(\pm\) 1.56 a  & 45.47 \(\pm\) 1.80 a  & 23.83 \(\pm\) 2.09 a  & 10.99 \(\pm\) 1.20 a  & 3.60 \(\pm\) 0.14 a  & 2.42 \(\pm\) 0.20 a  \\ All brines 2,3 & 15.93 \(\pm\) 2.03 a  & 50.45 \(\pm\) 9.05 a  & 17.88 \(\pm\) 3.31 b  & 7.27 \(\pm\) 2.45 b  & 4.37 \(\pm\) 2.66 a  & 4.10 \(\pm\) 3.39 a  \\ \hline Ratio meat to brine, 2 \({}^{\circ}\)C & 1 & 1.06 & 1.06 & 1.07 & 0.59 & 0.52 \\ Ratio meat to brine, 7 \({}^{\circ}\)C & 0.82 & 0.95 & 1.45 & 1.45 & 0.63 & 0.32 \\ Ratio meat to brine, 12 \({}^{\circ}\)C & 0.77 & 0.75 & 1.59 & 2.52 & 2.74 & 4.47 \\ \hline \end{tabular}  
* Area of all peaks with mass to charge (\(m/z\)) ratio exceeding 500 Da are considered as 100%; 2 Mean \(\pm\) SD; 3 Difference between values denoted by the same letter in column is statistically insignificant at \(p<0.05\) as judged by Student \(t\)-test; 4b means within the same row with different lowercase letters differing significantly.  
\end{table} 
Table 5: Relative areas of peaks on the MALDI-ToF MS spectra of meat and brine ISP extracts. tides to intact proteins. SDS-PAGE serves as a method for the analysis of polypeptides and proteins, but not oligopeptides.
## 3 Methodology
### Building of Adaptive Classification Model  
The master node is the centerpiece of the proposed model, receiving instances sent from all the edge nodes for adapting the classifier with these instances. First, the received data are tested for missing values, as these reduce the accuracy of the classification process. The fact is that the data received for different patients make it impractical to use arithmetic methods to impute the value, such as the average. In addition, the EEG signals often have unbalanced distribution in the target values, in which the number of normal signal values greatly exceeds the number of the seizure values, and this makes choosing the most frequent value to missing value imputation also impractical. Therefore, the process of imputation in the proposed model depends on the employment of machine learning to build a quick and simple classifier to infer the missing value through training on the rest of the known values.  
The imputation is performed by using an extreme fast decision tree classifier, which was proposed by [53]. It is an efficient modified version of VFDT in which the data will be split when there is a good splitting; then, if a better split is detected, the old one is replaced. The result will be tested for concept drift; if there is a drift in the distribution of the data, the new training operation starts. The ensemble classifier is built using an Adaptive Random forest.  
At this stage, the process begins with reading a specified number of data rows sent from all patients' devices, and this number represents the default size of the ADWIN window. The default window size must be carefully chosen because if the number is too small, the changes in the model will be frequent. In addition, if the window size is too large, the response of the model to the change in data distribution will be limited. The next step is using the online Bagging [32] of K-based classifiers, and each base classifier is constructed using VFDT, as proposed in [25]. In the beginning, this stage sets the size of the ARF model and then the number of subset ensembles generated by re-sampling the data samples from the previous stage. The number of subsets is equal to the number of base classifiers. Each subset is used to train a single VFDT classifier. Finally, each data element is classified by voting among all base classifiers.
# A Novel Digitized Method for the Design and Additive Manufacturing of Orthodontic Space Maintainers
## 4 Discussion
However, in this particular case, the space maintainer features a custom design and offers the potential for adhesive bonding, which could be advantageous when compared to conventional space maintainers. Cost implications of the proposed digital workflow are expected to be comparable to custom-made maintainers, with potential cost savings if 3D printing is conducted in-house. In addition, leveraging the expertise of clinicians who are already proficient in additive manufacturing, the application of these technologies for the manufacturing of 3D printed space maintainers would prove valuable in both primary care settings and dental hospitals/universities where these technologies have already been implemented. This study acknowledges that it is focused on an in vitro investigation and emphasizes the need for future in vivo studies. These studies will evaluate the acceptance of the device, debonding rate, plaque accumulation, and anatomical characteristics of teeth adjacent to the space. In vivo studies will provide a comprehensive understanding of the strengths and limitations of space maintainer designs. These studies will enable the evaluation of various designs for the first primary molar and second primary molar, encompassing both maxillary and mandibular regions. Furthermore, the studies will take into account the effects on patients' oral health status, including factors such as caries risk and oral hygiene level.
# Validating a 5G-Enabled Neutral Host Framework in City-Wide Deployments
## 3 Overview of Neutral Host Framework
### Infrastructure Layer  
The Infrastructure layer contains the resources in terms of computing, network, and radio components managed by the neutral host. This layer is graphically divided into several Network Functions Virtualization Infrastructure (NFVI) sections and access nodes to identify the distributed compute and radio architecture conceived for municipalities and infrastructure owners acting as 5G neutral host providers. Moreover, this framework is also aligned with 5G performance requirements by providing edge computing capabilities. This will result in real-time access to radio network information, thereby unlocking the potential of advanced future applications.
_Applied_  
_sciences_  
**Low-Voltage Ride-Through Techniques in**  
**DFIG-Based Wind Turbines: A Review**  
**[PERSON] 1,*(c)**, **[PERSON] 1(c)**, **[PERSON] 1, [PERSON] 2 and [PERSON] 1  
1 State Key Laboratory of Electrical Insulation and Power Equipment, and the school of Electrical Engineering, Xi'an Jiaotong University, Xi'an 710049, Shaanxi Province, China; [EMAIL_ADDRESS] (H.L.);  
[EMAIL_ADDRESS] (X.Z.); [EMAIL_ADDRESS] (W.L.)
2 Jiangsu Nuclear Power Co., Ltd., Lianyungang 222000, Jiangsu Province, China; [EMAIL_ADDRESS]
* Correspondence: [EMAIL_ADDRESS]; Tel.: +86-136-5925-0017  
**Abstract:** In recent years, considerable advances were made in wind power generation. The growing penetration of wind power makes it necessary for wind turbines to maintain continuous operation during voltage dips, which is stated as the low-voltage ride-through (LVRT) capability. Doubly fed induction generator (DFIG)-based wind turbines (DFIG-WTs), which are widely used in wind power generation, are sensitive to disturbances from the power grid. Therefore, several kinds of protection circuits and control methods are applied to DFIG-WTs for LVRT capability enhancement. This paper gives a comprehensive review and evaluation of the proposed LVRT solutions used in DFIG-WTs, including external retrofit methods and internal control techniques. In addition, future trends of LVRT solutions are also discussed in this paper.  
**Keywords:** Doubly fed induction generator; low-voltage ride through; wind turbine; rotor-side converter; grid-side converter
# A Self-Paced Multiple Instance Learning Framework for Weakly Supervised Video Anomaly Detection
## 4 Experiments
### Baseline Models and Implementation Details
#### 4.2.2 Implementation Details  
Following [7; 24], each video is divided into 32 or 200 segments, i.e., \(N\) = 32 for DeepMIL and RTFM (\(N\) = 200 for UR-DMU). The hyperparameters in the loss function are set to \(\alpha\) = 1 \(\times\) 10\({}^{-4}\), \(\beta\) = 8 \(\times\) 10\({}^{-3}\), and \(\gamma\) = 8 \(\times\) 10\({}^{-4}\). The maximum number of epochs \(T\) is set to 100, \(R_{0}\) = 3 and \(R_{T}\) = 32 for DeepMIL and RTFM (\(R_{T}\) = 200 for UR-DMU). The threshold value \(\eta\) is set to 0.5, and the late-fusion threshold value \(c\) is set to 0.4. The SP-MIL framework is trained on a single NVIDIA RTX 3090 GPU (NVIDIA Corporation, Santa Clara, CA, USA) using PyTorch (version 2.1). The Adam optimiser is employed with a weight decay of 0.0005 and a learning rate of 0.001. For DeepMIL, the fully connected layers described in our model have 512, 32, and 1 nodes, each followed by a ReLU activation function and a dropout function with a dropout rate of 0.7. For RTFM and UR-DMU, the number of nodes is 512, 128, and 1, respectively. It is worth noting that for fair comparison, we also reported the results of DeepMIL, RTFM, and UR-DMU reproduced by our framework by using different pre-trained features.
# On Classical Solutions for A Kuramoto-Sinelshchikov-Velarde-Type Equation
## 2 Proof of Theorem 1, under the Assumptions (2) and (4)
Multiplying (44) by \(2u\), an integration on \(\mathbb{R}\) gives  
\[\left\|u(t,\cdot)\right\|_{L^{2}(\mathbb{R})}^{2}= 2\int_{\mathbb{R}}u\partial_{t}udx\] \[= -4\gamma\int_{\mathbb{R}}u(\partial_{x}u)^{2}dx-2\delta\int_{ \mathbb{R}}u\partial_{x}^{3}udx-2\beta^{2}\int_{\mathbb{R}}u\partial_{x}^{4} udx\] \[-2\mu\int_{\mathbb{R}}u\partial_{x}^{2}udx-2\gamma\int_{\mathbb{ R}}u^{2}\partial_{x}^{2}udx\] \[= -4\gamma\int_{\mathbb{R}}u(\partial_{x}u)^{2}dx+2\delta\int_{ \mathbb{R}}\partial_{x}u\partial_{x}^{2}udx+2\beta^{2}\int_{\mathbb{R}} \partial_{x}u\partial_{x}^{3}udx\] \[-2\mu\int_{\mathbb{R}}u\partial_{x}^{2}udx+4\gamma\int_{\mathbb{ R}}u(\partial_{x}^{2}u)^{2}dx\] \[= -2\beta^{2}\left\|\partial_{x}^{2}u(t,\cdot)\right\|_{L^{2}( \mathbb{R})}^{2}-2\mu\int_{\mathbb{R}}u\partial_{x}^{2}udx.\]  
Therefore,  
\[\frac{d}{dt}\left\|u(t,\cdot)\right\|_{L^{2}(\mathbb{R})}^{2}+2\beta^{2} \left\|\partial_{x}^{2}u(t,\cdot)\right\|_{L^{2}(\mathbb{R})}^{2}=-2\mu\int_{ \mathbb{R}}u\partial_{x}^{2}udx. \tag{46}\]  
Due to the Young inequality,  
\[\left|u\right|\int_{\mathbb{R}}\left|u\right|\partial_{x}^{2}u|dx= 2\int_{\mathbb{R}}\left|\frac{uu}{\beta}\right|\left|\beta \partial_{x}^{2}u\right|dx\] \[\leq \frac{\mu^{2}}{\beta^{2}}\left\|u(t,\cdot)\right\|_{L^{2}(\mathbb{ R})}^{2}+\beta^{2}\left\|\partial_{x}^{2}u(t,\cdot)\right\|_{L^{2}(\mathbb{R})}^{2}.\]  
We prove (21). Thanks to (25), (45) and (48),  
\[\left\|u(t,\cdot)\right\|_{L^{2}(\mathbb{R})}^{2}= 2\int_{\mathbb{R}}u\partial_{t}udx\]0  
Thus, we obtain that  
\[\left\|u(t,\cdot)\right\|_{L^{2}(\mathbb{R})}^{2}= 2\int_{\mathbb{R}}u\partial_{t}udx\]1  
Arguing as in (52, Lemma 2.4) or (53, Lemma 2.3), we have (21). Finally, (47) follows from (21) and (48), while thanks to (45) and (47), arguing as in Lemma 2, we have (22). **Lemma 8**. : _Fix \(T>0\) and assume (4) or (5). There exists a positive constant \(C(T)>0\), such that_  
\[\left\|u(t,\cdot)\right\|_{L^{2}(\mathbb{R})}^{2}= 2\int_{\mathbb{R}}u\partial_{t}udx\]2  
_for every \(0\leq t\leq T\). Moreover, (28) holds._  
Proof. : Let \(0\leq t\leq T\). Multiplying (44) by \(2\partial_{t}u\), an integration on \(\mathbb{R}\) gives  
\[ \begin{split}\beta^{2}\frac{d}{dt}&\left\|\partial_ {x}^{2}u(t,\cdot)\right\|_{L^{2}(\mathbb{R})}^{2}+2\left\|\partial_{t}u(t, \cdot)\right\|_{L^{2}(\mathbb{R})}\\ &=-2\gamma\int_{\mathbb{R}}(\partial_{x}u)^{2}\partial_{t}udx-2 \delta\int_{\mathbb{R}}\partial_{x}^{3}u\partial_{t}udx\\ &\quad-2\mu\int_{\mathbb{R}}\partial_{x}^{2}u\partial_{t}udx-2 \gamma\int_{\mathbb{R}}u\partial_{x}^{2}u\partial_{t}udx.\end{split}  \tag{50}\]  
Due to (21) and the Young inequality,  
\[
# Distributed Task Allocation for Multiple UAVs Based on Swarm Benefit Optimization
## 5 Discussion  
### Computational Complexity  
The computational complexity of the proposed BV method is assessed using the same approach as for the PI method [39; 41; 42]. The core aspect of the BV method's complexity resides in the construction of its task plan, defined as follows:  
\[O((L_{i}-\overline{\overline{\mathbf{a}}_{i}})\overline{\overline{\mathbf{a} }_{i}}^{2})\varphi_{i}\varpi \tag{18}\]  
where \(L_{i}\) denotes the maximum number of tasks that UAV \(i\) can execute, \(\overline{\overline{\mathbf{a}}_{i}}\) signifies the number of tasks within the current task plan \(\mathbf{a}_{i}\) of UAV \(i\), \(\varphi_{i}\) represents the number of tasks currently available to UAV \(i\) that meet the constraints but have not yet been selected, and \(\varpi\) indicates the computational load required for UAV \(i\) to evaluate the individual benefit of a task.  
Both the proposed BV method and the traditional CBBA and PI methods share the same computational complexity, as expressed by Equation (18). Specifically, their computational complexities are polynomial and identical.
## 3 Results and Discussion  
### Morpho-Agronomic Characterization of Potato Accessions  
The results of the morpho-agronomic characterization of potato accessions are graphically shown in Figure 2. The average of four replicates for each accession, collected in Table 1 (Appendix A), and the descriptors were simultaneously projected onto the space of the first three principal components (PCs), accounting for 58.8% of the variance. Plots (a) and (b) of Figure 2 display the biplots in the PC1-PC2 and PC1-PC3 planes, respectively.  
Figure 2a reveals a neat separation of the TU potatoes and a clustering of the remaining samples into distinct groups separated along PC1: the first group collected the samples of three commercial  
Figure 2: Projection of the potato accessions and morpho-agronomic descriptors onto the (**a**) PC1–PC2 and (**b**) PC1–PC3 planes. Asterisks identify the accessions from the Majella National Park. Potato accessions are Gamberale (GA), Turchesa (TU), Montenerodomo (MO), Pizzoferrato (PI), Désirée (DE), Agria (AG), Kennebec (KE), and Spunta (SP). PC: Principal Component.  
varieties--AG, KE, and SP--while the latter was formed by the samples of the commercial potato DE together with those belonging to the three accessions grown in Majella National Park--MO, GA, and PI. Within each of these two groups, most of the samples belonging to different accessions overlapped, especially those of the first group. The descriptors with higher loadings on PC1, namely W, 23, N4, 30, 34, and 28 (defined in Table 1), were the morpho-agronomic traits that were more influential in the differentiation of the two clusters and the isolation of the TU accession. On the other hand, PC2 seemed to essentially describe the variability internal to the replicates of each potato accession, which was mainly associated with the N1, N2, and N3 descriptors. The samples of the three commercial potatoes--AG, KE, and SP--were still grouped together along PC3 (Figure 2b), while those belonging to the DE, MO, GA, PI, and TU accessions were instead well separated along this component. The morpho-agronomic traits 26, 14, 19, and 35 were mainly responsible for this differentiation. In summary, the samples of five potato varieties (MO, PI, GA, DE, and TU) clustered in distinct groups. Concerning the three commercial cultivars of AG, KE, and SP, variability within the replicates seemed, by contrast, to be greater than the differences among the varieties. Nevertheless, treatment of the morpho-agronomic data matrix by means of HCA revealed a clustering of potato samples, including AG, KE, and SP, into eight distinct groups (Figure 3), each corresponding to a given variety. Similarities among the various clusters roughly reflected the reciprocal position of the potato classes within the explored PC subspace.  
The better separation of AG, KE, and SP varieties in the HCA analysis compared to PCA was not surprising considering that some information on the morpho-agronomic characters may not be retained by the first three components selected in PCA, which explained less than 60% of variance.
## 3 Methodology
### Heterogeneous Stance Network  
#### 3.2.1 Network Definition  
As shown in Figure 2, the heterogeneous stance network (HSN) is designed to effectively capture indirect stance relationships prevalent in real-world discourse. Rather than assuming a direct connection between a post and its target, the network explicitly integrates intermediate entities to account for stance expression through references, interactions, or contextual factors. This structured design enables the network to capture richer semantic and relational information, enhancing interpretability and robustness, particularly in cases where the target is not explicitly mentioned. Moreover, even when a post explicitly states its stance toward the target, the network provides additional contextual enrichment, further refining the stance representation and improving detection accuracy.  
The network consists of the following types of nodes:  
* Posts: Each post that expresses a stance toward a target is represented as a node in the network.
* Targets: These nodes represent the subjects or entities toward which stance is expressed. Examples include political figures, organizations, or controversial topics.
* Entities: Entities extracted from posts enrich contextual understanding. Depending on the dataset characteristics, entity nodes can be further categorized. For instance, in the case of \"X\" (formerly Twitter) data, entity nodes include the following: (1) hashtags: represent hashtags used in posts, which often serve as implicit stance indicators; (2) users: represent user mentions, capturing relationships between different social media users; (3) named entities: represent named entities such as people, organizations, or locations mentioned in the text.  
Figure 2: Overview of our framework with two components: (1) heterogeneous stance network construction, where a stance-aware graph is generated using LLMs; and (2) multi-view stance detection, which integrates textual and graph-based representations with tailored methods for both supervised and zero-shot scenarios.  
Edges in the HSN represent relationships among these nodes and are categorized into three types:  
* [noitemsep,topsep=0 pt,parsep=0 pt,topsep=0 pt]
* Favor Edges: Indicating a positive or supportive stance.
* Against Edges: Indicating an opposing or negative stance.
* Neutral Edges: Indicating the absence of a strong stance.  
This classification aligns with the stance label scheme commonly used in benchmark datasets such as SemEval-2016 (Sem16) [1], where the stance labels include **favor**, **against**, and **none**. In our network, we replace **none** with **neutral** to better reflect its semantic meaning.
## 3 Materials and Method
### Optimization Process for Time Points  
_Gradient descent optimization_: For both models, the optimization process aims to minimize the MSE by adjusting the model parameters and the selected time points iteratively:  
\[\theta_{t+1}=\theta_{t}-\eta\cdot\
abla_{\theta}MSE \tag{5}\]  
where \(\theta\) represents the model parameters at time points, \(\eta\) is the learning rate, and \(\
abla_{\theta}MSE\) is the gradient of the MSE with respect to \(\theta\).  
Both models utilize a gradient descent-like approach to optimize the selection of time points, with adjustments made iteratively to minimize the distance between the original and modified signals:  
_Initial time points_: The collection of equidistant points is used to initiate the optimization process by providing a baseline from which the algorithm iteratively refines the time points to capture the essential features of the original signal.  
\[\mathrm{T}=\{t_{1},t_{2},\,\ldots\,,t_{n}\} \tag{6}\]  
where T represents the initial set of uniformly distributed time points.  
_Interpolated values_: The ECG values at these time points are interpolated to match the original signal's length:  
\[\hat{y}(t)=\mathrm{interp}(\mathrm{T},\mathrm{ECG\ values}) \tag{7}\]  
The interpolation method used was Cubic Splines interpolation for the proposed models.  
_Distance calculation_: The distance (error) between the original and interpolated signals is calculated using mean square error (\(MSE\)):  
\[MSE=\frac{1}{N}\sum_{i=1}^{N}(y_{i}-\hat{y_{i}})^{2} \tag{8}\]  
_Iterative adjustment:_ For each time point \(t_{i}\), adjust by small increments (\(\delta\) = \(-1\), 0, 1\(\backslash\)delta = \(-1\), 0, 1\(\delta\) = \(-1\), 0, 1) to find a new set of time points that minimize the \(MSE\):  
\[T^{\prime}=\{t_{1},t_{2}+\delta,\,\ldots\,,t_{n}\} \tag{9}\]  
The time points are updated if the new configuration results in a smaller MSE:  
\[\mathrm{If}\,MSE\big{(}T^{\prime}\big{)}<MSE(T)\,\,then\,\,T=T^{\prime} \tag{10}\]  
By iterating through these steps, the models refine the time points to achieve better alignment between the original and modified ECG signals, thereby optimizing the non-uniform sampling.