esa-sceva/satcom-chunk-collection huggingface dataset:
# Task-Aware Feature Composition for Few-Shot Relation Classification
## 4 Methodology  
In this section, we will introduce our proposed framework for dynamically composing task-aware features. In the following sections, we use the subscript \(q\) or \(s\) combined with a symbol to indicate that the combined symbol applies to a query or support sentence. If the symbol does not have \(q\) or \(s\) as its subscript, we indicate that the symbol applies to both query and support sentences. For example, we use \(x\) to represent the sentence of a query or support instance, while we use \(x_{q}\), \(x_{s}\) to indicate the sentence of a query and support instance, respectively. Furthermore, for simplicity, we discard the superscript \(n,k\) of \(s\) in Section 4.1. As illustrated in Figure 2, our model mainly consists of four components:  
* **Context Encoder.** Given a query or support instance and its mentioned entity pairs, we employ a context encoder similar to the MLMAN [12] to encode an instance into sentence representation.
* **Relation Prototypes and Task Embedding Module.** Taking the query and support sentence representations as input, by computing the match degrees between query and support representations and using the degrees as weights to combine support representations, we can obtain a prototype for each relation. After obtaining relation prototypes, we treat them as a whole and place a CNN encoder on top of them to extract task-specific representations, which are depicted as task embeddings.
* **Task-aware Feature Composition.** Based on the task-specific representation, we further propose two different guiding modes to dynamically refine and compose task-aware feature representations for each meta-task. Our guiding modes are based on a task-gated mechanism and gated feature combination, respectively.
* **Class Matching.** Once the task-specific information is incorporated into the composed query representation and prototypes, these feature representations are then used as input and fed to a matching module \(M(.,.)\) to measure the relationship between query and class prototypes.
# Mobility-Assisted Digital Twin Network Optimization over Industrial Internet of Things
###### Abstract  
Many real-world networks for the Industrial Internet of Things (IIoT) have diverse connectivity characteristics and real-time constraints imposed by industrial processing. In the context of digital twin networks (DTNs), a large number of IIoT devices may access the network and have a tremendous volume of data. A crucial element of these IIoT devices is mobility, which cannot be effectively solved because the number of IIoT devices connected is extremely large. IIoT devices in DTNs suffer from poor data transmission and link quality because of their mobility. In this paper, device-to-device (D2D) communication-based mobility-assisted digital twin networks are proposed, where edge computing is introduced to design an efficient mapping between the physical and virtual space. Then, we propose the architecture of data transmission for the D2D network to maximize the data rate for reliable connectivity among multiple mobile nodes based on IIoT. A Markov decision process (MDP) is formulated to maximize the data rate for multiple mobile nodes while maintaining the D2D communication link quality. The simulation results demonstrate the superiority of the proposed scheme over other comparable models.  
Industrial Internet of Things; digital twin networks; mobility; Markov decision process Article
# Assessing Cyber Challenges of Maritime Navigation
## 1 Introduction
While automation offers excellent benefits, it also introduces a set of corresponding cybersecurity-related risks [22; 30; 31]. The global maritime industry systems depend on satellite navigation, especially GPS. Of particular concern is the relative ease [32] by which these systems can be jammed (through denial of reception by a competing signal) or spoofed (through deliberate introduction of a false signal). Satellite navigation is a vital part of a wide variety of the shipboard, port, and even oil rig systems [32; 33; 34; 35; 36; 37; 38; 39], including the Automatic Identification System (AIS) that is vital for navigation safety [40]. This article is a close investigation of the landscape of both cyber threats and actual incidents in the maritime sector. Furthermore, it discusses the risks, the motives and likely entities behind the threats, and the impacts of an attack that can range far beyond the company being attacked. Finally, it recommends how cybersecurity could be improved in the maritime sector over time, and hopefully it might inspire further research work. The paper is organized as follows: Section 2 describes the methodology, Section 3 presents an analysis of the cybersecurity landscape in the maritime sector and practical research into an AIS spoofing event. Section 4 discusses significant findings and provides some recommendation, and Section 5 contains a summary and conclusions.
## 4 Conclusions  
The fatigue propagation of a surface crack in a round bar subjected to tension loads has been investigated by using crack growth circles. The present results demonstrate that the experimental method had good convergence speed and accurate prediction of crack shape patterns. The following conclusions can be drawn:  
* The crack growth circles method is developed for the surface cracks of a round bar, and the circles are tangent to both current and new crack fronts. In this way, good simulation accuracy can be achieved with fewer iterations.
* A three-parameter model with fewer shape restraints whose center is allowed to move along the vertical axis is built, and the shape change of a fatigue crack is predicted more precisely. The nominal aspect ratio of an ellipse, which is the ratio of the maximum crack depth to the chord length \(c\), \(b_{n}/c\), is considered, instead of the actual aspect ratio of an ellipse semi-axis.
* A relatively large crack growth increment can be used by adopting the equivalent stress intensity factor \(\Delta K_{e}\) based on the stress intensity factors along the current and new crack fronts.
* The crack propagation process is described accurately based on the ratio of vertical growth toward the horizontal surface. It can be seen that the crack propagation paths differ with different initial flaws, but will converge asymptotically. The ratio of crack growth is always less than 1 for the case of initial crack \(b_{0}/a_{0}=1\), and the crack growth along the vertical central line is always greater than the growth toward the horizontal surface. For the case of an initial crack \(b_{0}/a_{0}=0\), a greater Paris law exponent m value generates more drastic change.
* The present solutions are compared with other numerical solutions and experimental data. Comparison shows that the present solutions agree well with the experimental data and are better than other numerical solutions.  
Conceptualization, S.C. and Y.Y.; methodology, S.C. and Y.Y.; software, Y.Y.; validation, S.C. and Y.Y.; formal analysis, S.C. and Y.Y.; investigation, H.C.; resources, H.C.; data curation, Y.Y.; writing--original draft preparation, S.C. and Y.Y.; writing--review and editing, S.C. and Y.Y.; visualization, S.C. and Y.Y.; supervision, S.C.; project administration, S.C.; funding acquisition, S.C. and H.C.  
This research was funded by the Natural Science Foundation of Shanghai (18 ZR1416500) and Development Fund for Shanghai Talents.  
The Authors declare no conflicts of interest.  
Figure 14: Crack propagation patterns compared with experimental data.
## 2 Materials and Methods
### Data Set  
Each instance, or observation, in the data set consists of 1729 readings of radiation reflection in the wavelength range of 400-750 nm taken at 0.21-0.22 nm intervals (predictor variables) describing 179 _A. thaliana_ plants grouped into the ground truth categories (dependent variables). The class distribution is as follows: class S0--120 observations (all the plants well-watered, from 21, 28 and 33 DAS); class S1--29 observations (water deficit for seven days, from 28 DAS); and class S2--30 observations (water deficit for 12 days, from 33 DAS). All the reflectance values were used as input features to the machine learning algorithms, with no feature selection.
###### Abstract  
Railway infrastructure faces significant operational threats due to ground deformation risks from natural and anthropogenic sources, posing serious challenges to safety and maintenance. Traditional monitoring methods often fail to capture the complex spatiotemporal patterns of railway deformation, leading to delayed responses and increased risks of infrastructure failure. To address these limitations, this study introduces InSAR-RiskLSTM, a novel framework that leverages the high-resolution and wide-coverage capabilities of Interferometric Synthetic Aperture Radar (InSAR) to enhance railway deformation risk prediction. The primary objective of this study is to develop an advanced predictive model that accurately captures both temporal dependencies and spatial susceptibilities in railway deformation processes. The proposed InSAR-RiskLSTM framework integrates Long Short-Term Memory (LSTM) networks with spatial attention mechanisms to dynamically prioritize high-risk regions and improve predictive accuracy. By combining image-based spatial attention for deformation hotspot identification with advanced temporal modeling, the approach ensures more reliable and proactive risk assessment. Extensive experiments on real-world railway datasets demonstrate that InSAR-RiskLSTM achieves superior predictive performance compared to baseline models, underscoring its robustness and practical applicability. The results highlight its potential to contribute to proactive railway maintenance and risk mitigation strategies by providing early warnings for infrastructure vulnerabilities. This work advances the integration of image-based methods within cyber-physical systems, offering practical tools for safeguarding critical railway networks.  
InSAR; railway deformation; risk prediction; LSTM; spatial attention +
Footnote †: journal: Applied sciences
# Ad Creative Discontinuation Prediction with Multi-Modal Multi-Task Neural Survival Networks
## 6 Case Studies
### Discussion of the Case Studies
#### 6.4.1 RQ1: Perspective of CPA Ratio  
In Table 7, we confirmed that both single- and multi-task models outperform the performance of the human operator. Because the performance of our models does not use unseen future data, which are used in the human performance, we posit that the same level of the performance is sufficient. This result suggests that, even if we decide the discontinuation using the prediction of our framework, the CPA efficiency does not get worse.  
Clearly, the performance after ad discontinuation cannot be known, and thus, when the prediction result comes after the actual discontinuation date, the CPA ratio is the same as the actual CPA ratio. We posit that this problem does not affect our purpose, as our proposal is not to outperform but rather support human decision-making.  
_To answer_ **RQ1**_, when our framework discontinues an ad creative, the CPA efficiency is almost equal to human operation._
# Application of Blockchain Technologies in Verification of Software Metrics
## 3 Methodology
### Validation of the Metrics
The blockchain commitment layer, denoted as B, stores the proof of metric compliance, the SHA-256 hash of the source file, the decentralized submitter identity, a timestamp, and a reference to the constraint profile. Finally, the decision function, denoted as R, maps the verification outcome to a qualitative result. It returns one of two values: valid submission or threshold violation detected. In our case, we demonstrate the practical application of the enhanced Blockchain-Based Metric Validation Scheme (BMVS) using zero-knowledge proofs (ZKPs), applied to a real-world Python project: TextAdventure. The goal is to validate whether a submitted source file satisfies predefined software quality thresholds while preserving privacy and ensuring tamper-proof verification via blockchain. The procedure is presented in the following pseudocode. function validate_source_file(file_path, threshold_profile):  # Step 1: Extract metrics from source code  metrics = extract_metrics(file_path)  file_id = hash_file(file_path)Step 2: Check local constraint satisfaction (optional pre-check) if not satisfies_thresholds(metrics, threshold_profile): print(\"Warning: Local validation failed. Proceeding with ZKP anyway.\")  
Step 3: Generate ZKP based on metric constraints zkp_proof = generate_zkp(metrics, threshold_profile)  
Step 4: Prepare metadata submission = { fileID: file_id, proof: zkp_proof, thresholdSetID: hash_threshold_profile(threshold_profile), timestamp: current_timestamp(), submitterDID: get_submitter_id() }
###### Abstract  
Mg\({}_{2}\)SiO\({}_{4}\) holds promise for its application in the microwave communication field due to its low dielectric constant and high Q\(\times\)_f_ value. However, its high negative \(\tau_{f}\) and high sintering temperature limit its application in low-temperature co-fired ceramic (LTCC) devices. In this work, Li\({}_{2}\)TiO\({}_{3}\) and CaTiO\({}_{3}\) were introduced to improve the \(\tau_{f}\), and LiF was chosen to decrease the sintering temperature. According to XRD patterns and SEM micrographs, the ceramic systems displayed a complex-phase structure, and the microstructure was densified when CaTiO\({}_{3}\) was added. All of the relative densities, dielectric constants, and Q\(\times\)_f_ values first increased and then decreased as the sintering temperature increased. The MLLC11.5 ceramics sintered at 800 \({}^{\circ}\)C could be obtained with the highest Q\(\times\)_f_ value of 54,581 GHz (at 8.06 GHz), \(\epsilon_{r}\) of 14.13, and \(\tau_{f}\) of + 5.81 ppm/\({}^{\circ}\)C. Furthermore, it was proven that the MLLC11.5 powders could be co-fired without any reaction with Ag powders at 800 \({}^{\circ}\)C, indicating its potential for LTCC application. The MLLC11.5 composition was used to prepare a GPS antenna and showed good prospects for its application in electronic communications.  
Mg\({}_{2}\)SiO\({}_{4}\)-Li\({}_{2}\)TiO\({}_{3}\)-LiF-CaTiO\({}_{3}\) ceramics; structure; microwave dielectric property; LTCC; antenna +
Footnote †: journal: _crystals_
# Fault-Tolerant Control for Carrier-Based Aircraft Based on Adaptive Fuzzy Sliding-Mode Method
## 3 Landing Environment
### Autonomous Carrier Landing System (ACLS) Control Framework
The function of APCS is to keep the angle of attack or velocity constant via autothrottle control in order to directly transform variations in pitch angle into variations in flight-path angle and enhance the flight-path control characteristic of the CBA during glide and landing. Consequently, the flight-path control performance of the CBA can be improved. This study directs the APCS controller to output the appropriate \(\delta_{T}\) to keep the velocity constant. Meanwhile, the function of AFSMFTC is to complete the tracking and control for the expected attitude angle by controlling \(\delta_{a}\), \(\delta_{e}\) and \(\delta_{r}\). To eliminate the influences of adverse factors on the control effects, an adaptive function and a sliding-mode fuzzy controller are designed in this study to compensate for the efficiency and performance loss of actuators and various unknown perturbations, respectively.
# A Trust-Influenced Smart Grid: A Survey and a Proposal
## 3 NIST Conceptual Domain Model
### Distribution Domain  
The distribution domain is electrically connected between the transmission domain and the customer domain. The electrical distribution system may be structured in a varied number of ways such as meshed, looped or radial--and each structure affects the reliability of the system. Initially, the communications interfaces within this domain were unidirectional and hierarchical, but now they work in a bi-directional manner. Typical applications within this domain are measurement and control, substation, DERs, distribution generation and storage.
## 3 Results and Discussion
### Quantification of Ginsenosides from the Roots, Stems and Leaves, and Berries of Panax ginseng
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline
**No.** & **Ginsensosides** & **Roots** & **Stems and Leaves** & **Berries** & **No.** & **Ginsensosides** & **Root** & **Stems and Leaves** & **Berry** \\ \hline
1 & gRf & \(1.8\pm 0.1\) & \(0.3\pm 0.1\) & \(3.6\pm 1.1\) & 21 & Ro & \(0.15\pm 0.01\) & \(0.014\pm 0.002\) & \(0.014\pm 0.002\) \\
2 & nR1 & \(0.06\pm 0.01\) & \(0.03\pm 0.01\) & \(1.2\pm 0.2\) & 22 & M-Rc & \(0.9\pm 0.1\) & \(1.08\pm 0.04\) & \(4.7\pm 0.5\) \\
3 & Rg1 & \(3.1\pm 0.2\) & \(9.6\pm 0.5\) & \(8.9\pm 4.3\) & 23 & Rb2 & \(0.6\pm 0.1\) & \(2.8\pm 0.1\) & \(2.3\pm 0.6\) \\
4 & Re & \(3.0\pm 0.3\) & \(17\pm 1\) & \(26\pm 8\) & 24 & Rb3 & not detected & \(0.5\pm 0.1\) & not detected \\
5 & vR4 & \(0.007\pm 0.001\) & \(1.2\pm 0.1\) & \(0.9\pm 0.1\) & 25 & M-Rb2 & \(0.9\pm 0.1\) & \(1.6\pm 0.1\) & \(6.8\pm 0.5\) \\
6 & Rf & \(4.6\pm 0.2\) & \(0.30\pm 0.01\) & \(3.4\pm 1.1\) & 26 & Rd & \(0.15\pm 0.01\) & \(7.1\pm 0.2\) & \(2.3\pm 0.5\) \\
7 & nR2 & \(1.1\pm 0.1\) & \(0.7\pm 0.1\) & \(4.5\pm 1.3\) & 27 & M-Rd & \(0.014\pm 0.001\) & \(3.1\pm 0.1\) & \(5.3\pm 0.4\) \\
8 & nR4 & \(0.14\pm 0.02\) & not detected & not detected & 28 & CO & \(0.06\pm 0.01\) & \(12.6\pm 0.1\) & \(0.4\pm 0.1\) \\
9 & F5 & not detected & \(5.8\pm 0.2\) & \(2.8\pm 0.3\) & 29 & Rg4 & not detected & not detected & \(0.06\pm 0.03\) \\
10 & Rh1 & \(0.021\pm 0.002\) & \(0.24\pm 0.01\) & \(0.04\pm 0.02\) & 30 & F4 & not detected & \(0.4\pm 0.1\) & \(2.1\pm 0.8\) \\
11 & Rg2 & \(0.17\pm 0.01\) & \(0.61\pm 0.03\) & \(0.6\pm 0.5\) & 31 & F2 & \(0.0019\pm 0.0006\) & \(2.7\pm 0.3\) & \(0.03\pm 0.01\) \\
12 & F3 & not detected & \(5.3\pm 0.2\) & \(4.1\pm 0.4\) & 32 & Rg3 & \(0.004\pm 0.0002\) & \(0.09\pm 0.01\) & \(0.007\pm 0.001\) \\
13 & Rg2-R & not detected & \(0.008\pm 0.001\) & \(0.06\pm 0.03\) & 33 & Mc & not detected & \(0.24\pm 0.04\) & not detected \\
14 & F1 & not detected & not detected & \(0.005\pm 0.001\) & 34 & CY & not detected & \(0.08\pm 0.02\) & not detected \\
15 & Ra2 & \(3.2\pm 0.1\) & not detected & not detected & 35 & CK & \(0.0010\pm 0.0004\) & \(0.003\pm 0.001\) & \(0.002\pm 0.001\) \\
16 & Ra3 & \(0.9\pm 0.1\) & not detected & not detected & 36 & Rk1 & not detected & \(1.18\pm 0.02\) & \(1.153\pm 0.004\) \\
17 & Rb1 & \(3.8\pm 0.3\) & \(0.08\pm 0.01\) & \(2.8\pm 0.6\) & 37 & Rg5 & \(0.031\pm 0.004\) & not detected & \(0.08\pm 0.01\) \\
18 & M-Rb1 & \(4.7\pm 0.5\) & \(0.7\pm 0.1\) & \(9.9\pm 1.2\) & 38 & Rb2 & not detected & \(0.011\pm 0.002\) & not detected \\
19 & Rc & \(0.9\pm 0.1\) & \(3.5\pm 0.1\) & not detected & 39 & Rh1-R & not detected & \(0.0028\pm 0.0004\) & not detected \\
20 & Ra1 & \(0.6\pm 0.2\) & not detected & not detected & & & & \\ \hline \end{tabular}
\end{table} 
Table 2: Quantification of 39 ginsenosides from the roots, stems and leaves, and berries of _P ginseng_. Values are expressed as mg/g.
# Multifunction RF Systems for Naval Platforms
## 2 Requirements for Naval RF Functions
### Communication Function Requirements
Because the angle of fall of the raindrops along the propagation path of the SATCOM signal is unknown, extreme signal fading due to an unfortunate alignment of linear signal polarization with the raindrop shape may occur. This situation is mitigated with circular polarization. Finally, TCDL employs circular polarization due to the difficulty of ensuring that shipboard and airborne antennas remain sufficiently aligned with each other during the communication link to allow linear polarization to be used. Shipboard antennas are fully stabilized in all three degrees of freedom, but airborne TCDL antennas are often stabilized in only azimuth and elevation to reduce the physical size and weight of the antenna assembly, particularly for a platform like a helicopter or unmanned aerial vehicle (UAV) where space is scarce. An example of this is the Honeywell AC-27 airborne TCDL antenna [19]. Consequently, certain types of motion experienced by the airborne platform with this type of antenna could result in excessive misalignment between the airborne and shipboard antenna orientations, leading to signal fade if linear polarization is employed. Use of circular polarization avoids this problem.
# Identifying Terpenoid Biosynthesis Genes in _Euphoria maculata_ via Full-Length cDNA Sequencing
The cytosolic MVA pathway begins with 2-Acetyl-CoA, which is converted to IPP by stepwise enzyme-mediated reactions [11]; the plasidial MEP pathway starts with the condensation of pyruvate and glyceraldehyde-3-phosphate by 1-deoxy-D-xylulose-5-phosphate (DOXP) synthase. Then, DOXP is converted to DMAPP by stepwise enzymatic reactions [12]. The IPP and DMAPP isomers are interconverted by isopentyl pyrophosphate isomerase (IDI) [13]. While triterpenoids and sesquiterpenoids are synthesized via the MVA pathway, monotereponoids, triterpenoids, and tetraterpenoids are synthesized via the MEP pathway [14]. Numerous reports document the terpenoids in _Euphorbia_ species. [PERSON] and [PERSON] (2011) isolated 18 terpenoid molecules from whole plant parts of _E. sapini_ by simple acetone extraction and deciphered their molecular structures [15]. Terpenoids were extracted from _E. padroi_, and an isolated tetracyclic triterpenoid was demonstrated to be a multidrug resistance reverser [16]. Many _Euphorbia_ species produce a milky latex that is irritating to humans and animals. The triterpen alcohols derived from the milky latex of _E. azorica_ have potential as chemopreventive and chemotherapeutic agents in cancer treatment [17]. [PERSON] et al. (2018) isolated 17 triterpenoid derivatives including two lancostane-type triterpenoids from _E. maculata_[4]. The isolated triterpenes exhibited potent anti-inflammatory activities, and the authors proposed these triterpenes as candidate cancer chemopreventive agents. Terpenoids have pharmacological benefits, including antitumor, anti-inflammatory, antibacterial, antioxidation, and immunoregulation activities, and can be used in the prevention of cardiovascular diseases [18]. A transcriptome is the complete set of transcripts at a defined spatial and temporal stage of an organism's life cycle, and it provides comprehensive information on gene expression and regulation [19; 20]. Next-generation sequencing (NGS) technologies, such as Illumina paired-end transcriptome analysis [21; 22] and single-molecule real-time sequencing (PacBio SMRT) technology, have been used to isolate numerous key genes in metabolite biosynthesis pathways [23; 24]. The PacBio SMRT system is especially useful for plants lacking reference genome sequence data because it reads full-length transcripts [25; 26; 27]. Plant metabolites are often biosynthesized in specific tissues; thus, tissue-specific transcriptomes can be compared to identify key genes involved in various complex metabolite biosynthesis pathways in plants [28; 29]. In this study, we characterized the terpenoid biosynthesis genes in _E. maculata_. We sequenced the leaf, root, and stem transcriptomes using Illumina short-read sequencing and PacBio SMRT techniques. The former technique allowed us to identify differentially expressed genes (DEGs) in the metabolite biosynthesis pathways, and the latter allowed us to obtain the complete sequences and isoform copy number information of transcripts involved in terpenoid biosynthesis.