esa-sceva/satcom-chunk-collection huggingface dataset:
# Deep Learning-Based Automatic Summarization of Chinese Maritime Judgment Documents
## 4 HybridSumm: A Hybrid Summarization Model for Maritime Judgment Documents
### RoBERTa-DCNN-GR: An Extractive Summarization Model
#### 4.1.4 Gated Linear Unit (GLU) and Residual Mechanism  
GLU [64] employs a dual-branch parallel architecture to construct a dynamic gating mechanism through differentiated linear transformations, regulating the flow intensity of feature information. By adopting a feature filtering strategy, this mechanism suppresses redundant signals while effectively preserving gradient propagation paths. Such a design not only mitigates the gradient vanishing problem, but also significantly enhances the model's ability to model long-range dependencies within sequential data.  
Based on this, we propose a GLU-integrated dilated convolution optimization module to capture long-distance features in maritime judgment documents. The module takes sentence embeddings \(\mathbf{X}\) generated by RoBERTa-wwm-ext as input and processes features in parallel via dual-branch dilated convolutions, as shown in Equation (2).  
\[\mathbf{Y}=Conv1d_{1}(\mathbf{X})\otimes\sigma(Conv1d_{2}(\mathbf{X})) \tag{2}\]  
where \(conv1d_{1}\) performs a linear mapping on the input to preserve basic semantic features, while \(conv1d_{2}\) generates a context-aware gating weight matrix through non-linear transformation and the Sigmoid function (\(\sigma\)). The two branches are dynamically fused via element-wise product (\(\otimes\)), producing the optimized feature representation \(\mathbf{Y}\).  
To further improve gradient propagation stability and enhance the model's capability for complex semantic modeling, we introduce a residual mechanism [65] into the gated dilated convolution module, as shown in Equation (3).  
\[\mathbf{Y}=\mathbf{X}+Conv1d_{1}(\mathbf{X})\otimes\sigma(Conv1d_{2}(\mathbf{X })) \tag{3}\]  
Since \(conv1d_{1}\) uses no activation function (only linear transformation), Equation (3) can be equivalently reformulated as Equation (4) [66].  
\[\mathbf{Y}=\mathbf{X}\otimes[1-\sigma(Conv1d_{2}(\mathbf{X}))]+Conv1d_{1}( \mathbf{X})\otimes\sigma(Conv1d_{2}(\mathbf{X})) \tag{4}\]  
Let \(\alpha=\sigma(Conv1d_{2}(\mathbf{X}))\) denote the spatially adaptive dynamic weight matrix that controls feature fusion ratios. Equation (4) is then simplified to Equation (5).  
\[\mathbf{Y}=\mathbf{X}\otimes(1-\mathbf{\alpha})+Conv1d_{1}(\mathbf{X})\otimes\mathbf{\alpha} \tag{5}\]  
As shown in Equation (5), the input sequence \(\mathbf{X}\) undergoes dual-branch parallel processing. In the residual branch, the original input is element-wise multiplied by the dynamic weight 1--\(\mathbf{\alpha}\) to preserve shallow-layer features. Meanwhile, in the transformation branch, \(\mathbf{X}\) is first processed through a dilated convolutional layer (\(conv1d_{1}\)) to extract high-order semantic features, which are then element-wise multiplied by the dynamic gating weight \(\mathbf{\alpha}\) to enable feature selection and enhancement. The final output \(\mathbf{Y}\) is generated by direct summation of both branch results.
# Translation-Based Embeddings with Octonion for Knowledge Graph Completion
## 2 Related Work
### Translation-Based Methods  
In translation-based methods, the entities are usually regarded as points in vector space, and the relations are represented as the translation vectors between two entities. The \(l1\)-norm or \(l2\)-norm is used as the scoring function to judge the rationalities of the triples by measuring the distance from translated subject entities to object entities. The smaller the distance is, the more reliable the triple is.  
TransE [9] is an epoch-making translation-based method. For a given triple \((h,r,t)\), TransE assumes that the true triple satisfies \(\mathbf{h}+\mathbf{r}\approx\mathbf{t}\), where \(\mathbf{h}\), \(\mathbf{r}\), \(\mathbf{t}\) are the vector representations of \(h\), \(r\), and \(t\), respectively. However, TransE has some defects, such as poor handling of 1-to-N, N-to-1, N-to-N relations, and the incapability to model symmetric relations. Subsequently, a number of TransE-derived methods are proposed to improve the problems with TransE. TransH [21] regards each relation as a hyperplane, and the entity embedded vectors are projected into the corresponding relational hyperplane. TransR [22] uses the relation-specific projection matrix \(\mathbf{M}_{r}\) instead of TransH's hyperplane to project the entity embedded vectors into space, but this will bring high consumption of resources. TransD [23] models the relational mapping matrix \(\mathbf{M}_{re}\) in a more flexible way. STransE [24] defines that each relation no longer has only one projection matrix, but carries out different transformations for the head and tail entities individually. Therefore, besides the translation vector \(\mathbf{r}\), each relation in STransE has two projection matrices \(\mathbf{M}_{r,h}\) and \(\mathbf{M}_{r,t}\).  
Due to the relatively simple structure, translation-based methods tend to have poor representation capability, especially in modeling complex relations in KGs. Nevertheless, for the advantage of being efficient with low computational cost, they still have value for further research. In this paper, we continue to focus on the translation-based methods, and enhance the translational KGC framework by introducing the mathematical theories of octonion and hyperbolic geometry. This enables our methods to maintain the advantages of being lightweight and training-friendly, while enhancing the representation capability, which is, to some extent, more promising than the following semantics-based and neural network-based methods.
# Numerical Investigation of the Nonlinear Composite Action of FRP-Concrete Hybrid Beams/Decks
###### Abstract  
Interfacial slip can cause rigidity degradation and stress concentration in fiber-reinforced polymer-concrete hybrid beam (FCHB). Therefore, precisely evaluating the composite action between fiber-reinforced polymer (FRP) and concrete of FCHB plays a pivotal role in structural analysis and design. Previous push-out tests showed that most connections for FCHB behave nonlinearly in load-slip relationships even at a low load level. However, existing analytical equations have their limitations due to the assumption of linear load-slip interfacial relationship which is not suitable for FCHB. The originality of this paper is to propose a finite difference method (FDM) to elaborate the interfacial slip and shear stress. FDM agreed well with the analytical solutions of the linear load-slip relationships for connections. Results indicate that higher accurateness can be obtained by using more elements. And 40 elements for half span of FCHB can reduce the error of numerical results to 1%. Then, the proposed FDM was expanded to predict the interfacial behavior of FCHB considering nonlinear interfacial load-slip relationships. It was found that perforated FRP ribbons can ensure nearly full composite action and the bolted connection can lead to a very high slip level. The use of ultra-high performance concrete (UHPC) results in a higher degree of composite action than normal concrete. The deflection considering slip was computed by adding deformation under full composition action and that caused by the slip effect. It was suggested that high strength steel bolts are effective both in normal concrete and UHPC. When the slip modulus is suggested to be larger than 20 kN/mm, the capacity per bolt should be larger than 20 kN.  
FRP-concrete hybrid beam (FCHB); composite action; finite difference method (FDM); nonlinear interfacial load-slip relationship +
Footnote †: journal: Applied Mechanics and Physics Letters
# A _uv_-Cellular Network: Design, Implementation and Experiments
## 5 Experiments Study  
As the experiment case study, we implement the network with twenty underwater network nodes and one onshore node in the real environment. The main task of the network is to enable monitoring of the target area by collecting data from underwater sensors. At the same time, users can control the network nodes by sending various instructions. Protocols in each layer are implemented to ensure that the \(uw\)-Cellular network can be applied to the targeted scenarios.
# Rapid Deployment Method for Multi-Scene UAV Base Stations for Disaster Emergency Communications
## 2 System Model
### Channel Model
Due to the requirement of setting a certain redundancy value for path loss in disaster scenarios, this paper does not consider the path loss generated by the transmission and reception signals between the UAV and the ground user equipment. The relationship curve between path loss \(P_{loss}\) and horizontal distance \(D\) is plotted using Equations (1) to (3), as shown in Figure 2:  
When the path loss \(P_{loss}\) is less than or equal \(T_{1}\) to the threshold, it can be inferred that the quality of customer service (QoS) of all ground users [39] under the coverage of UAV \(i_{th}\) in need of rescue is ensured.
## 6 Results and Discussion  
The numerical results are based on the computations of the analytically formulated scattered and transmitted fields for a radially stratified cylindrical human arm model outlined in the previous sections. The human arm model is illuminated by a line source located at the point \((x_{1},x_{2})=(0.13,0)\) m at a single frequency. The scattered field outside the arm model is calculated on a circle of radius 0.3 m and inside the arm model on a circle of radius 0.025 m. An unhealthy arm model having bleeding or a tumor is simulated with an object \(\mathcal{D}\) representing the abnormal region placed on the origin in the innermost layer. The object \(\mathcal{D}\) is assumed to have a square cross-section and its wave number is taken as 1.5 times the wave number of the innermost layer, i.e., \(k_{obj}=1.5\,k_{5}\). The added object is chosen of the size \(1\times 1\) cm and it is divided into \(10\times 10\) cells. The first three examples are devoted to the analysis of the background, which is, in our study, taken as the matching medium, by taking into account its constitutive parameters, namely, its permittivity and conductivity, and the frequency of the source. Here, as a basic approach in our computations, one of the three parameters is supposed to be constant, and the amplitude variation of the functional \(F(f,e_{r\,\mathrm{mm}},\sigma_{\mathrm{mm}})\) depending on the other two parameters, which are defined on the appropriate ranges, is depicted as an image plot. Points selected from the relevant colored regions in the image plot will indicate the lowest and the highest amplitude values of the functional. To accurately simulate the electrical properties of the human arm tissues at the frequencies from 100 MHz to 2 GHz, the data generated using the three-pole Debye equation provided in Equation (1) are employed in numerical calculations.
## 2 Parameters and Model  
In this paper, a semi-active HESS with direct current (DC)/DC and ultra-capacitor connection is selected by weighing the cost, efficiency, and control difficulty of the various structures of HESS [25], as described in Figure 1. Table 1 lists the basic parameters of the electric vehicle.  
This paper chooses a lithium iron phosphate battery and its basic parameters are listed in Table 2. Battery life is one of the most crucial indexes for the economic performance of HESS. In order to evaluate the battery life, it is essential to set up an accurate capacity loss model of the battery.  
Referring to the semi empirical model of capacity loss for a lithium iron phosphate battery in the literature [26], and the modified semi empirical model [27; 28], the capacity loss model of a lithium iron phosphate battery is established, as illustrated in Equation (1), where operating temperature, the influences of the battery discharge rate, the battery discharge depth, and the discharge time on the capacity loss of battery are taken into account.  
\[Q_{loss}=B_{1}e^{\frac{-313297}{RT}}\times\left(\sqrt[0.58]{(1.169e^{-0.3375n}+ 0.146e^{0.1271n})}\times e^{(-0.1494+0.1494n)}\times A_{Int}\right)^{0.55} \tag{1}\]  
where \(Q_{loss}\) represents the capacity loss of the battery, \(R\) denotes the ideal gas constant, and \(T\) is the Kelvin temperature of the battery operating, \(K\). \(A_{Int}\) is the Ah-throughput, Ah. \(n\) presents the charge rate and \(B_{1}\) is the pre-exponential factor.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Total weight/kg & 1900 \\ Curb weight/kg & 1500 \\ Front section/m\({}^{2}\) & 2.3 \\ Aerodynamic drag factor & 0.29 \\ Rolling resistance & 0.012 \\ Wheel radius/m & 0.307 \\ Motor rated power/kW & 80 \\ Motor peak power/kW & 105 \\ Motor voltage class/V & \(\leq\)360 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Basic parameters of vehicle.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Index** & **Value** \\ \hline Nominal capacity/Ah & 20 \\ Nominal voltage/V & 3.2 \\ Internal resistance/m\(\Omega\) & \(\leq\)6 \\ Weight/g & 514 \(\pm\) 10 \\ Charge voltage/V & 3.65 \(\pm\) 0.05 \\ Discharge termination voltage/V & 2.0 \\ Operating temperature/\({}^{\circ}\)C & \(-\)20 \(\sim\)60 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Basic parameters of a lithium iron phosphate battery.  
Figure 1: Semi-active structure with direct current (DC)/DC and ultra-capacitor connection.  
Generally, it is considered that the battery life is terminated when the battery capacity reaches 20% of its nominal capacity. The life loss of battery, \(L_{loss}\), is indicated in Equation (2).  
\[L_{loss}=\frac{A_{ln}}{A_{ln(20\%)}} \tag{2}\]  
where \(A_{ln(20\%)}\) represents the Ah-throughput when the battery capacity arrives at 20% of its nominal capacity.
## 5 Experiments and Results
### Data Collection and Evaluation Metrics
\begin{table}
\begin{tabular}{c c} \hline \hline
**Description** & **Corpus** \\ \hline Number of Valid Ransomware Versions & 35,000 \\ \hline Number of Malware Versions & 500 \\ \hline Number of Good-ware Versions & 500 \\ \hline Data Archives & [40; 41; 42] \\ \hline Aggregation Time & 1/1/2019-1/9/2019 \\ \hline Training Data Corpus & 17,332 \\ \hline Testing Data Corpus & 17,666 \\ \hline \multirow{6}{*}{Ransomware Families} & AiDS & 4000 \\ \cline{2-3}  & GpCode & 8000 \\ \cline{2-3}  & Archiveus & 1500 \\ \cline{2-3}  & WinLock & 3620 \\ \cline{2-3}  & Reveton & 2400 \\ \cline{2-3}  & CryptoLocker & 1720 \\ \cline{2-3}  & CryptoWall & 3250 \\ \cline{2-3}  & RaaS & 1300 \\ \cline{2-3}  & Cerber & 1535 \\ \cline{2-3}  & Locky & 2000 \\ \cline{2-3}  & Crysis & 1320 \\ \cline{2-3}  & WannaCry & 1300 \\ \hline Sopra & 1570 \\ \hline Zeus & 1500 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The corpora of data. Thus, overall performance metrics are used during performance evaluation task that involves comparative experiments, and realistic experiments. Such experiments quantify how often and how long the proposed machine learner and the multi-tier ransomware streaming analytics model takes for ransomware detection and ransomware families' categorization versus the dominant machine learners and the benchmarking anti-ransomware tools. Both comparative and real-time experiments are evaluated by using the corpora of training and testing data. It is worthy of mention that simulated machine learners in Weka, as well as soft computing by Python, are used for the conducted experiments.
## 2 Methods
### Robotic System for Thyroid Surgery  
#### 2.4.1 Master-Slave Control System for Thyroid Surgery  
In consideration of surgical safety, the proposed thyroid surgical robot adopts a master-slave control paradigm, allowing the surgeon to precisely control the robotic arm's movements through a master console. The master device used in this study is the Omega.7, manufactured by Force Dimension. The master device and the slave arm have different structures and operate within a master-slave framework. Since the base coordinates of the master device and the slave arm do not coincide and their workspaces are not identical, an incremental mapping method is necessary. This paper proposes an incremental master-slave control strategy based on Cartesian space, and its control strategy is illustrated in Figure 5a.  
Figure 5: Robotic system for thyroid surgery. (**a**) Master–slave control strategy. (**b**) Performance of tremor filtering for the master hand. (**c**) Trajectory of the master hand device when constrained by spherical and linear safety zones.  
Thyroid tumor resection surgery requires high precision, where even the slightest hand tremors can significantly impact the accuracy of the procedure. The sampling frequency of the master device is 100 Hz, and the frequency of the surgeon's physiological hand tremors is approximately 8-12 Hz [28]. To improve surgical precision, a second-order band-stop Butterworth filter [29] was applied to eliminate the surgeon's hand tremors. The transfer function of the Butterworth filter in the discrete-time domain is given by:  
\[H(z)=\frac{b_{0}+b_{1}z^{(-1)}+b_{2}z^{(-2)}}{a_{0}+a_{1}z^{(-1)}+a_{2}z^{(-2)}} \tag{15}\]  
where \(a_{i}\) and \(b_{i}\) are coefficients of the filter. Their values are as follows: \(a_{0}=1\), \(a_{1}=-1.45\), \(a_{2}=0.78\); \(b_{0}=0.89\), \(a_{1}=-1.45\), \(a_{2}=0.89\).  
The performance of tremor filtering for the master hand is shown in Figure 5b. The blue curve represents the original position signal of the master hand device, while the red curve shows the position signal after applying tremor filtering. The red curve exhibits smoother behavior compared to the blue curve, highlighting the effectiveness of the filtering process.
## 4 Performance Evaluation Results and Discussion
### Experiments and Results
Then, the metric values for each class were averaged to obtain the precision, sensitivity, specificity, and f1-score for each dataset. In each of the five experiments, the values of accuracy, precision, sensitivity, specificity, and f1-score were summarized in the form of a table. In particular, the accuracy values in each table and the training time measured during the model training phase were visualized in a graph form.
# Defect R-CNN: A Novel High-Precision Method for CT Image Defect Detection
###### Abstract  
Defect detection in industrial computed tomography (CT) images remains challenging due to small defect sizes, low contrast, and noise interference. To address these issues, we propose Defect R-CNN, a novel detection framework designed to capture the structural characteristics of defects in CT images. The model incorporates an edge-prior convolutional block (EPCB) that guides to focus on extracting edge information, particularly along defect boundaries, improving both localization and classification. Additionally, we introduce a custom backbone, edge-prior net (EP-Net), to capture features across multiple spatial scales, enhancing the recognition of subtle and complex defect patterns. During inference, the multi-branch structure is consolidated into a single-branch equivalent to accelerate detection without compromising accuracy. Experiments conducted on a CT dataset of nuclear graphite components from a high-temperature gas-cooled reactor (HTGR) demonstrate that Defect R-CNN achieves average precision (AP) exceeding 0.9 for all defect types. Moreover, the model attains mean average precision (mAP) scores of 0.983 for bounding boxes (mAP-bbox) and 0.956 for segmentation masks (mAP-segm), surpassing established methods such as Faster R-CNN, Mask R-CNN, Efficient Net, RT-DETR, and YOLOv11. The inference speed reaches 76.2 frames per second (FPS), representing an optimal balance between accuracy and efficiency. This study demonstrates that Defect R-CNN offers a robust and reliable approach for industrial scenarios that require high-precision and real-time defect detection.  
defect detection; CT images; deep learning; nuclear graphite components +
Footnote †: journal: Applied sciences
# Role of Ion-Exchange Resins in Hydrogen Reactions
## 3 Ion-Exchange Resins Application to Hydrogenation Reactions
### Hydrogenation of Alkenes
The metals employed (Pd, Pt, Ru, and Rh) were incorporated into AmberlystTM 15 in different amounts from 0.5 to 2 wt.% to obtain monometallic catalysts. A bimetallic Pd-Pt catalyst was also tested. The highest conversions (i.e., up to 99%) and selectivity towards cyclohexane (i.e., \(>\)99%) were reported for Pd-based catalysts. Results obtained with Pt, Ru, and Rh catalysts were clearly less favorable with, conversion values in the range of 1-4%. Some relevant hydrogenation reactions of alkenes found in the covered literature are shown in Scheme 2. Table 3 lists resin types, tested reactions, and obtained results of some of the reviewed works. As Scheme 2 and Table 3 reveal, Pd is the preferred metal to hydrogenate C=C bonds, as described in previous works [85; 86], but Ru-based catalysts have also been used to hydrogenate stronger C=C bonds, such as the conjugated ones in benzene rings. Regarding the IERs, both synthetic and commercial resins have been used, but mostly sulfonated and gel-type ones. Noteworthy, the anion exchange resin tested by [PERSON] and coworkers yielded no significant catalytic activity in several hydrogenation reactions [67]. Different strategies regarding the activation step of the metal catalyst emerge from the revised literature, including chemical reduction of the ionic metal with NaBH\({}_{4}\) and in situ reduction during reaction conditions (with no separate pre-reduction step). Depending on the author and explored reaction, the reported results differ substantially. For instance, [PERSON] reported much higher conversions when pre-reducing the catalyst at 1 MPa H\({}_{2}\) in water at 30 \({}^{\circ}\)C for 2 h compared to in situ reduction during cyclopentene hydrogenation to cyclopentane (Table 3), whereas [PERSON] reported the opposite behavior in the hydrogenation reaction of methyl 2-acetamidoacrylate to methyl acetylalaninate [67; 84].