esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
Figure 5: Results of field experiments—BBUNPROWS application displayed (**a**) vehicle location, (**b**) primary/caution alert, and (**c**) secondary/warning alert ahead of a worker (left images) and a passing vehicle (right images). During the 50 repetitions of the above-described experiment, 50 true-positive primary and secondary alert scenarios were observed, whereas the number of false positives equaled 7 and 5 with regard to issuance of primary and secondary alerts, respectively. Zero false negatives were observed. The above results are summarized in Table 4. In accordance with these results, the observed accuracy of the proposed BBUNPROWS application with regard to the issuance of primary/caution and secondary/warning equaled 93% and 95%, respectively.
# Enhancement of Question Answering System Accuracy via Transfer Learning and BERT
## 4 Method
### Model for Predicate Mapping  
Due to the variety of natural language questions, different expressions may correspond to the same question intent, and the same subject entity may also generate different predicates, which poses a major challenge to the domain KBQA task. Such as matching between the question \"Who wrote the journey to the West\" and \"Author \". The predicate matching model is suitable for matching the predicates in the question with the predicates in the knowledge base, understand the intent of question, and select the predicate that best matches the question. First, the entity disambiguation results can reduce the size of candidate predicates set, so we start from the sample of candidate entities acquired in the entity disambiguation task and retrieve the set of predicates of that entity in the knowledge base as candidate predicates set. Next, we notice that in answering the question \"Who wrote the journey to the West?\", the information of the candidate predicates can be enriched by adding the information of the first-degree chained predicates of the candidate answer entities retrieved by the candidate predicates. For example, \"The candidate entities of Journey to the West include release date, director, author, etc.\" are related to the candidate predicate \"author\".  
Therefore, this paper proposes a BERT-Softmax model [11] (as shown in Figure 9) for entity-predicate matching. We treat BERT-Softmax as a binary sequence classification problem, where the output label is 1 for candidate relation samples that accurately reflect the intent of question and 0 for those inaccurately reflecting the aim of question. The input data are composed of two portions: the question and predicate. The input data of the question part consist of [CLS], a sequence of question characters with the entity character replaced by the entity character, [SEP], the candidate relation, and [SEP], which is encoded by the BERT network to acquire the hidden vector of the last four encoder layers, stitched together and input to the Softmax layer for classification. The output label is 0 or 1. The  
Figure 8: Entity Disambiguation model.  
loss function is also the cross-entropy loss function, which was minimized during training. In the prediction, the probability of predicting a candidate relationship as label 1 is used as the score of candidate relationship.
## 2 Materials and Methods
### Data Analysis  
The Kolmogorov-Smirnov test was used to assess the normality of the distribution. Basic descriptive statistics are presented as mean and standard deviation (SD) for normally distributed variables or as the median and interquartile range (25 th-75 th) for not normally distributed variables. To examine the differences between the loading conditions, a one-way repeated measures ANOVA or the Friedman test were used. Where significant main effects were observed, a modified Bonferroni _post-hoc_ procedure was calculated to observe significant differences between each load condition. Partial eta squared was presented to define'small' (0.01),'medium' (0.06), and 'large' (0.14) effect size. Partial eta squared represents a measure of a given association which is often described as the proportion of total variation explained by an independent variable, and variance from other predictor variables from the total non-error variance. All statistical analyses were performed by using SPSS v23.0 software (IBM, Armonk, NY, USA) with an alpha level set a priori at \(p<0.05\) to denote statistical significance.
# Scaled Model Simulation and Experimental Verification of Submarine Flexible Pipeline Laying System
## 2 Similarity Theory and Scaled Model of Flexible Pipeline-Laying System
### Similarity Principle and Derivation of Similar Norms
\tag{18}\]  
In the design and manufacture of actual models, the model size and material must be selected according to the site of the required experiments. Therefore, the similar constants of geometric size, elastic modulus, and material density (\(C_{l}\), \(C_{E}\), \(C_{\rho}\)) can be determined, and the remaining similar constants can be calculated from these constants. The scaled model uses a scale of 1:20 according to the laboratory conditions. The material used in the prototype is _S690 QL_ high-strength welded structural steel. The material of the model is _Q235_ steel. The calculated modulus of elasticity has a similar ratio of 0.996, and the similarity ratio of density is 0.999. The similarity relationship and similarity of the model are shown in Table 1.  
To determine the similarity constants of wave loads, the gravity similarity of fluid flow phenomena needs to be considered, and the Froude number is selected as the similarity criterion [27]. If the fluid is subjected to a mass force of gravity only, the Froude number (Fr) of the model can be expressed as Equation (19). \[F_{rm}=\sqrt{\frac{F_{i}}{F_{g}}}=\sqrt{\frac{\rho l^{2}v^{2}}{\rho gl^{2}}}= \frac{v_{m}}{\sqrt{g_{m}l_{m}}} \tag{19}\]  
where the parameters with angle label m represent the parameters of the model, and those with angle label p represent the parameters of the prototype. \(g\) represents the acceleration of gravity, \(l\) represents the length, \(\rho\) represents the density of the fluid, \(v\) represents the velocity of fluid flow, and \(F_{i}\) and \(F_{g}\) represent inertial force and gravity, respectively. From the similarity criteria of the model and the prototype, \(F_{rm}=F_{rp}\), and the gravity is constant, \(g_{m}=g_{p}\), we can get Equation (20). \[\frac{v_{m}}{v_{p}}=\sqrt{\frac{l_{m}}{l_{p}}} \tag{20}\] \begin{table}
\begin{tabular}{c c c} \hline \hline
**Physical Quantity** & **Similarity Relation** & **Ratio** \\ \hline Stress \(\sigma\) & \(C_{\sigma}=C_{E}\) & 0.996 \\ Strain \(\varepsilon\) & \(C_{\epsilon}=1\) & 1 \\ Elastic modulus \(E\) & \(C_{E}\) & 0.996 \\ Density \(\rho\) & \(C_{\rho}\) & 0.999 \\ Length \(l\) & \(C_{l}\) & 1/20 \\ Concentrated Load \(F\) & \(C_{E}\)C\({}_{l}\)\({}^{2}\) & 0.0025 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of similarity ratios of experimental models. It can be derived that, for time \(t\), there is Equation (21). \[\frac{t_{m}}{t_{p}}=\sqrt{\frac{l_{m}}{l_{p}}} \tag{21}\]  
Therefore, the period and amplitude of the motion response of the ship to wave load can be reduced in proportion to time and length to obtain the required motion environment for the model.
# Optimization Techniques for Design Problems in Selected Areas in WSNs: A Tutorial
## 11 Routing in Multi-hop Single Immobile Sink for Different Objectives under Distance Uncertainties
### Problem Statement and Design Objectives  
For the three different types of problems, energy consumption was considered. The transmission and reception energy for each node is accounted for after normalizing with respect to the radio energy dissipation of the transmitter and receiver circuits. The expression for the total normalized energy has two components:  
* one for the normalized received energy which is equivalent to the number of received bytes, i.e., \[\sum_{j|(j,i)\in A}f_{ji}\] (24)
* and one for the the normalized transmitted energy, which is equivalent to the number of transmitted bytes times a linear function in the transmission distance, i.e., \[\sum_{j|(j,i)\in A}f_{ji}\left(1+\beta d_{ij}^{2}\right)\] (25) where
* \(A\) is the set of nodes in the network,
* \(f_{ij}\) is the number of transmitted bytes from node \(j\) to node \(i\).
* \(d_{ij}\) is the distance from node \(i\) to node \(j\) and \(\beta\) is a constant depending on transceiver parameters.
# Scheduling of Industrial Control Traffic for Dynamic RAN Slicing with Distributed Massive MIMO +
## 5 Optimization Formulations  
In our previous work [16], we minimized the maximum number of pilots used in any slot. This reflects a static resource allocation, in which the industrial control traffic is given a fixed (but minimal) number of pilots, and this allocation applies to all slots. However, it is more efficient to allocate pilots dynamically, if the radio access network supports this. In this case, the industrial control traffic is allocated only to the pilots it requires in any given slot, and all remaining pilots can be used for other traffic. We now consider this case, and therefore, optimize it for the minimal number of pilots used per slot. This gives a measure of efficiency under dynamic slicing that is independent of the frame length.
## 4 Results and Discussion
### Results Using TF-IDF Features and the SMOTE Technique
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Models** & **Accuracy** & **Class** & **Precision** & **Recall** & **F1 Score** \\ \hline \multirow{4}{*}{LR} & \multirow{4}{*}{0.93} & 0 & 0.91 & 0.99 & 0.95 \\ \cline{3-5}  & & 1 & 0.98 & 0.93 & 0.95 \\ \cline{3-5}  & & \(-1\) & 0.94 & 0.72 & 0.81 \\ \cline{3-5}  & & Macro avg & 0.94 & 0.88 & 0.90 \\ \hline \multirow{4}{*}{RF} & \multirow{4}{*}{0.95} & 0 & 0.91 & 0.99 & 0.95 \\ \cline{3-5}  & & 1 & 0.98 & 0.93 & 0.94 \\ \cline{3-5}  & & \(-1\) & 0.95 & 0.78 & 0.86 \\ \cline{3-5}  & & Macro avg & 0.95 & 0.90 & 0.92 \\ \hline \multirow{4}{*}{DT} & \multirow{4}{*}{0.95} & 0 & 0.95 & 0.95 & 0.96 \\ \cline{3-5}  & & 1 & 0.97 & 0.95 & 0.96 \\ \cline{3-5}  & & \(-1\) & 0.86 & 0.85 & 0.84 \\ \cline{3-5}  & & Macro avg & 0.93 & 0.92 & 0.92 \\ \hline \multirow{4}{*}{KNN} & \multirow{4}{*}{0.62} & 0 & 0.99 & 0.55 & 0.71 \\ \cline{3-5}  & & 1 & 0.30 & 0.96 & 0.45 \\ \cline{3-5}  & & \(-1\) & 0.41 & 0.92 & 0.57 \\ \cline{3-5}  & & Macro avg & 0.56 & 0.81 & 0.58 \\ \hline \multirow{4}{*}{SVM} & \multirow{4}{*}{0.95} & 0 & 0.93 & 0.99 & 0.96 \\ \cline{3-5}  & & 1 & 0.98 & 0.95 & 0.96 \\ \cline{3-5}  & & \(-1\) & 0.89 & 0.80 & 0.84 \\ \cline{3-5}  & & Macro avg & 0.94 & 0.92 & 0.92 \\ \hline \multirow{4}{*}{AdaBoost} & \multirow{4}{*}{0.78} & 0 & 0.68 & 1.00 & 0.81 \\ \cline{3-5}  & & 1 & 0.97 & 0.59 & 0.73 \\ \cline{3-5}  & & \(-1\) & 0.87 & 0.62 & 0.72 \\ \cline{3-5}  & & Macro avg & 0.84 & 0.74 & 0.76 \\ \hline \multirow{4}{*}{GNB} & \multirow{4}{*}{0.78} & 0 & 0.91 & 0.63 & 0.74 \\ \cline{3-5}  & & 1 & 0.87 & 0.77 & 0.82 \\ \cline{3-5}  & & \(-1\) & 0.65 & 0.93 & 0.77 \\ \cline{3-5}  & & Macro avg & 0.81 & 0.78 & 0.78 \\ \hline \multirow{4}{*}{ETC} & \multirow{4}{*}{0.89} & 0 & 0.90 & 0.92 & 0.91 \\ \cline{3-5}  & & 1 & 0.93 & 0.90 & 0.91 \\ \cline{3-5}  & & \(-1\) & 0.73 & 0.79 & 0.76 \\ \cline{3-5}  & & Macro avg & 0.86 & 0.87 & 0.86 \\ \hline \multirow{4}{*}{SGD} & \multirow{4}{*}{0.94} & 0 & 0.92 & 0.99 & 0.95 \\ \cline{3-5}  & & 1 & 0.98 & 0.94 & 0.96 \\ \cline{3-5}  & & \(-1\) & 0.94 & 0.81 & 0.87 \\ \cline{3-5}  & & Macro avg & 0.95 & 0.91 & 0.93 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Results using BoW features and the SMOTE technique.
# GREG: A Global Level Relation Extraction with Knowledge Graph Embedding
## 3 Model
### Synchronization Process  
The synchronization process's main objective is to fine-tune the local relation extraction module's classifier by end-to-end fashion. This enables the classifier to properly classify both global relation embeddings and local relation vector representations consistently. The synchronization process largely consists of two parts: calculating error margin \(E\) for each document and applying feedback to the classifier. The error margin indicates the difference between the generated knowledge graph from the knowledge graph constructor and the ground truth knowledge graph from the training dataset.  
To calculate the error margin, we used a toned-down graph edit distance [21] shown in Equation (3), where the cost of each graph edit operation \(c(e_{i})\) is always 1:  
\[GED(g_{\sigma^{\prime}},g_{T})=\underset{(e_{1},e_{2},...,e_{k})\in P(g_{\sigma^ {\prime}},g_{T})}{min}\sum_{i=1}^{k}c(e) \tag{3}\]  
In Equation (3), \(g_{\sigma^{\prime}}\) indicates the generated knowledge graph while \(g_{T}\) is the ground truth knowledge graph. During this phase, any missing edges (entities) or vertex (relations) in the knowledge graphs are marked before the next phase begins. Usually, the missing entities from the generated knowledge graph \(g_{\sigma^{\prime}}\) indicate two possibilities: that the entity was not detected by the NER module during the preprocessing stage, or that no relations were found connected to it. For the other relation errors, an external memory is given to store all the entities, local relation representation, and correct labels in pairs.  
In the following feedback phase, the relation classifier from the first module starts fine-tuning. Then, the fine-tuning will take place to increase the performance of the classifier. However, to synchronize the classifier's fine-tuning while training the knowledge graph embeddings from our Knowledge graph module, we added an anomaly system. When an anomaly occurs, the external memory is checked to see whether errors involving the current input exist. If an error with the input class involved is detected, then any other involving relation vector representations that were paired to it are immediately concatenated to the current input's relation vector representation--thus allowing the model not only to fine-tune itself but correct the errors found during the previous phase. The re-occurrence of whether an anomaly happens is based on each error margin. The higher the error margin was for each error, the higher it will reoccur during fine-tuning. Once the fine-tuning process is done, then we return to phase 1 and repeat until the error margins are minimized. For this process, the individual fine-tuning process of phase 2 was always limited to 200 epochs to avoid any possible overfitting--allowing the constructed knowledge graph to become close to an ideal knowledge graph state similar to the ground truth knowledge graph as shown in Figure 4.
## 6 Safety Mechanisms Enabled by AI Agents
### Emergency Coordination and Autonomy During Crisis  
Emergencies at sea--ranging from equipment failures and fire to man-overboard incidents and extreme weather--demand immediate, coordinated responses. In such high-stakes scenarios, autonomous vessels cannot rely solely on remote human intervention or pre-programmed rules. Instead, AI agents act as the vessel's frontline crisis coordinators, capable of perceiving, interpreting, and initiating emergency actions in real time.  
Upon detection of an anomaly or external distress signal, the AI agent fuses inputs from the following sources:  
* Onboard sensors (e.g., engine status, structural strain gauges, bilge water levels);
* Environmental detectors (e.g., barometric pressure, wave height);
* Communication systems (e.g., distress calls, AIS alerts).  
These multimodal signals are interpreted using computer vision, natural language processing, and multi-agent reasoning frameworks. The agent assesses not only the severity of the threat but also contextual elements, including vessel type, location, proximity to rescue assets, and ongoing mission status [113].  
Consider a case where a sudden hull breach is detected mid-ocean. The AI agent triangulates internal pressure data, water ingress readings, and hull vibration patterns. Within seconds, it will perform the following actions:  
* Deploy bilge pumps;
* Reroute propulsion to stabilize heading;
* Broadcast automated Mayday messages via AIS and satellite;* Provide a real-time diagnostic feed to the nearest rescue coordination center.  
Unlike static emergency systems, AI agents can orchestrate parallel actions, such as evacuating crew zones (if present), isolating compartments, triggering fire suppression, or initiating controlled deceleration, while adapting to unfolding events.  
They also enhance human oversight during crisis. Real-time dashboards and alert summaries ensure that remote operators or port authorities receive intelligible, prioritized insights, not overwhelming raw data. This synergy between AI and humans enables faster, more effective joint decisions under pressure [114; 115].  
Furthermore, AI agents can simulate scenario outcomes, forecasting the impact of action sequences (e.g., whether to anchor, reverse thrust, or engage passive drift) and selecting those that maximize survival, minimize damage, or buy time until rescue arrives.  
In emergencies, the agent's role evolves from navigator to crisis commander, managing complexity, minimizing reaction time, and preserving the ship's integrity without hesitation or confusion.
## 3 Application of the Proposed MSPSO Algorithm to the IPTBK Problem
### Basic Idea  
Assume that multiple feasible attack paths exist among the nodes (\(v_{i}\),...,\(p_{k}\),...,\(p_{l}\)). The fitness value of each route was calculated to examine whether the particle selects a low-cost routing path. Typically, a path-search algorithm is used to enable travel routing systems to increase their efficiency by considering the selection of low-cost network routing, namely (i) the shorter distance between two nodes (\(x_{i}\), \(x_{j}\)) and \(d_{ij}\)(hop count) and (ii) the better quality of service (QoS) between nodes (\(x_{i}\), \(x_{j}\)) (i.e., QoS\({}_{ij}\)). Thus, the path-search algorithm usually selects a specific path with the smallest routing cost (i.e., least distance travelled and high QoS to decrease the routing time). Typically, the routing cost of path (\(C_{i}\)) for node \(x_{i}\) to the victims is inversely proportional to the distance travelled and directly proportional to the QoS (i.e., low delay and traffic congestion), i.e., \(C_{i}=f\left(QoS,\frac{1}{d_{ij}}\right)\). Theoretically, solving the problem of multimodal optimisation requires seeking the minimum cost function of \(L_{\text{p}}\) subject to the constraint of routing cost. The minimum cost function of \(L_{\text{p}}\) is a positive number (\(\sum_{j=1}^{n}C_{i}\cdot e_{ij}>0\)) that is expressed as follows:  
\[\mathit{Fitness}=L\text{p}=\sum_{j=1}^{n}C_{i}\cdot e_{ij}^{x}, \tag{6}\]  
\[\begin{array}{l}\mathit{Min}\ L\text{p},\ \forall i,j\\ \mathit{Subject}\ to\ \sum_{j=1}^{n}C_{i}\cdot e_{ij}^{x}>0,\end{array} \tag{7}\]  
where \(\mathit{Fitness}\) represents the fitness value of a path. The \(\mathit{Fitness}\) is used for assessing the suitability of each path, and \(e_{ij}^{x}\) indicates whether a path exists from node \(i\) to node \(j\) for particle \(x\). An \(e_{ij}^{x}\) value of 1 indicates that a connected path exists from node \(i\) to node \(j\) for particle \(x\), whereas an \(e_{ij}^{x}\) value of 0 indicates that no connected path exists.  
In the proposed MSPSO scheme, the path-search algorithm uses a best-first search strategy formulated using weighted graphs to discover the lowest-cost path to the given goal node. In general, it achieves this aim by examining a tree of paths originating at the start node and extending these paths one edge at a time until its termination criterion is satisfied.  
Cluster-first, route-second strategy: Inspired by the capacity-constrained vehicle routing problem and its cluster-first, route-second solution (CFRS) strategy, this study divided the attack sources into several network areas based on IP domains and the time from DNS log data. Moreover, several particle subwarms were allocated in each area in sequence. One might expect to obtain the optimal solution in the entire domain with a heuristic algorithm. The advantage of the proposed strategy is that the attack sources are divided into several areas in advance by performing clustering analysis on routing traffic, which can reduce the redundancy of reconstructing the attack path.
## 1 Introduction  
Antibiotic resistance has become a major public health concern. According to the Centers for Disease Control and Prevention (CDC), infections due to multidrug-resistant (MDR) bacteria are responsible for one death every 15 min in the US [1], and the most recent analysis estimates that in 2019 nearly 5 million deaths worldwide were associated with bacterial resistance, including 1.27 million deaths directly attributable to MDR bacteria [2]. _Escherichia coli_ and _Staphylococcus aureus_ are the two main pathogenic bacteria involved in deaths associated with drug resistance. The World Health Organization (WHO) estimates that the number of deaths due to antibiotic resistance could even reach 10 million by 2050 if no action is taken, including the development of new effective therapies [3]. The overwhelming majority of current antibiotics are derived from microorganisms, but surprisingly little attention has been paid to alternative resources such as macromycetes. Only recently, pleurmutilin derivatives from Basidiomycete fungi have been approved for human use in 2019 by the Food and Drug Administration (FDA). Yet mushrooms represent a remarkable and original reservoir of antibacterial compounds that remains to be investigated deeper [4].  
To unveil promising fungal species in the fight against pathogenic bacteria, we have evaluated the antibacterial potential of 70 mushroom extracts from 31 species growing in North East France. In order to accelerate the identification of promising species, we have implemented thin-layer chromatography-direct bioactivity (TLC-DB). All extracts were evaluated against five pathogenic wild-type bacteria, including _Staphylococcus aureus_, _Escherichia coli_ and _Pseudomonas aeruginosa_, listed as priority pathogens by the WHO, and for which there is an urgent need to identify new antibiotics [3]. The most promising mushroom extracts were subsequently evaluated by TLC-DB towards your MDR strains of _S. aureus_ and your MDR strains of _E. coli_, obtained from clinical isolates. These screenings revealed that various fungal compounds inhibited the growth of MDR bacteria. Two mushroom species, the polypore _Fomitopsis pinicola_ and the common earthball _Scleroderma citrium_, were found to be the most interesting fungi regarding the overall number and selectivity of active compounds. Stearic acid has been identified as a common constituent contributing greatly to the antimicrobial activity of mushroom extracts.