esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract
Among this information, the recommendation sentences are the main focuses and desired contents that need to be extracted from CPG. These contents assist the domain experts in making evidence-based decisions. The field of text classification and information extraction has greatly benefited from advances in computing, producing a plethora of algorithms, tools, and applications, based on machine-learning and pattern-based approaches. [9; 10; 11; 12; 13; 14]. However, in the clinical domain, most of the natural language processing tasks including, guideline processing and information extraction, are still using pattern-based approaches [15]. Pattern-based approaches perform better than machine-learning models in clinical text classification [16]. The patterns are generally extracted by human experts based on their heuristics [17]. An expert focuses on the sequence of terms used in content for patterns, therefore the terms used in patterns suffer from the problems of polysemy and synonymy [18]. To overcome this problem, we proposed a machine-learning assistive pattern-based approach, which consists of heuristic patterns, part-of-speech (POS) patterns, and unified medical language system (UMLS) patterns for CPGs sentence classification to recommendation sentences (RS) and non-recommendation sentences (NRS). A group of experts extracted the initial heuristic patterns from an annotated guideline based on their heuristics using a group-based decision-making method known as nominal group technique (NGT) [19]. The NGT is selected due to its effectiveness in a group decision-making process. Simultaneously, we apply supervised machine-learning algorithms such as decision tree and rule induction, and unsupervised algorithm including Latent Dirichlet Allocation (LDA) and word2 vec [20; 21]. We selected the aforementioned algorithms because of their effectiveness and decision transparency. These algorithms provide a list of words, which are mainly contributing to a classification decision. We evaluated and analyzed all contributing words considered by machine-learning and finalized a list of salient terms. We provided the salient terms list to the participating experts to review their extracted patterns by considering those salient terms as well. The experts revised the patterns which increased the sentence classification accuracy. The proposed approach has two-fold benefits. It presents disease-specific information to physicians, which helps in providing standardized clinical services. It can also be used in annotating CPG sentences for computable CPGs generation. The rest of the article is structured as follows. Section 2 describes related work. Section 3 provides the detail of the proposed solution. Section 4 describes results with discus sion, Section 5 evaluates the proposed methodology, and finally, Section 6 concludes the study.
## 3 Results and Discussion
### Research Design and Discussion
#### 3.4.1 Concrete Performance of \"Fresh Networking\"
The overall score for the service quality evaluation was 70.98, which ranks as \"Satisfactory\", in accordance with the evaluation grade. The overall evaluation results show that consumers' overall perception of the \"Fresh Networking\" supply chain service quality is \"satisfied\", and the ratings of the first-tier indexes of capital flow service capability, logistics service capability, and information flow service capability were 71.82, 72.18, and 70.22, respectively, which also reach the \"satisfaction\" level. The ratings of the first-tier indexes of capital flow service capability, logistics service capability, and information flow service capability were 71.82, 72.18, and 70.22, respectively, which also reach the \"satisfaction\" level, thus indicating that the supply chain service quality is relatively high and customer satisfaction is high. At the same time, it should also be observed that the service capability of business flow in the first-level index was 67.34, which is rated as \"average\", and thus, it needs to be further improved to enhance the collaborative processing capability of business flow.
# Routing in Mobile Wireless Sensor Networks: A Leader-Based Approach+
## 5 Conclusions  
We have presented a routing approach for mobile wireless sensor networks, which is based on the election of a leader node and the construction of a spanning tree with the root in the sink node, which is mobile as well. On the basis of local information about link quality, leader elections are performed in order to restructure the spanning tree so that message delivery rates could be optimised.  
We have designed two protocols to implement the approach, LBR1, and LBR2. Both protocols use a heartbeat mechanism to react to node mobility. However, while LBR1 is fully reactive, in the sense that leader-election messages are forwarded immediately providing a good delivery rate, LBR2 buffers the messages to be forwarded periodically, resulting in a more effective performance at the price of higher delays.  
We have simulated our protocols in Castalia and carried out comparative evaluations with respect to two other MWSN protocols, AODV and RASeR, to determine the behaviour of the protocols with respect to their scalability and their resilience to mobility, measured as the node velocity. In general, our protocols outperform the other two.  
Although we have not identified a specific application scenario for our approach, evaluation results show that it can be applied to MWSN including hundreds of elements that move freely in areas  
Figure 8: End-to-end delay results about resilience to mobility. Results obtained for 64 nodes. Note the logarithmic scale on the \(y\) axis for Figure 8b. (**a**) Hops to reach the sink (average); (**b**) End-to-end delay (average).  
Figure 7: End-to-end delay results about scalability (network size). Results for a maximum velocity of 20 m/s. Note the logarithmic scale on the \(y\) axis for Figure 7b. (**a**) hops to reach the sink (average); (**b**) end-to-end delay (average).  
of a size in the order of at least tens of hectares, with velocities of up to 25 m/s. This includes practical applications in smart cities or person and animal tracking, among others.  
The approach has the potential for great scalability. Since, in our protocols, a node is conscious of its connectivity and only sends messages when it is connected, message traffic could be optimised by using buffering and opportunistic communication. In order to apply to a specific scenario, our approach brings a combination of possibilities for finding the appropriated configurations that fits the trade-offs among the different parameters, such as protocol version (LBR1 or LBR2), heartbeat frequency (which has a high impact on reaction times and henceforth on mobility resilience), transmission range, or packet size.  
For future work, we will also focus on extensions of the approach oriented to the use of multiple sinks and the combination of static and dynamic elements, in order to face high-scale problems.
# Vulnerability Assessment of Sensor Systems
## 2 Background and Related Work
### Common Criteria Primer  
There are three basic processes in the CC [5] methodology:  
* the IT security development process of an IT product called Target of Evaluation (TOE); after different security analyses, a document is prepared, called Security Target (ST); the ST embraces the security problem definition (SPD) and its solution by specifying security objectives (SO), security requirements, and functions; security functional requirements (SFRs), derived from SOs, describe how security measures should work in the operational environment; EAL-related security assurance requirements (SARs) determine how much assurance one can have in the IT product; the ST comprises TOE security functions (TSFs) which meet SFRs; the TSFs are implemented on the claimed EAL;
* the TOE development process (according to the SAR components embraced by the claimed EAL) concerns the IT product (TOE) and its documentation;
* the security evaluation process conducted according to CEM in an independent laboratory (ITSEF), supervised by the given national certification body [6].  
The results of the first two processes are called evaluation evidences. They are transferred together with the TOE to the ITSEF.  
Part 2 of the CC standard [5] comprises components to express security functional requirements (SFRs) used to model the IT product's security behavior. Part 3 of the CC standard [5] includes components to express security assurance requirements (SARs) of this product. The components described in both parts are grouped by families, which, in turn, are grouped by classes, e.g.:  
* the \"FDP\" functional class concerns the user data protection, its family \"FDP_ACC\" concerns the access control policy, and the family component \"FDP_ACC.2\" expresses the issue of \"Complete access control\";
* the assurance class \"ATE\" concerns tests, its family \"ATE_DPT\" (depth of testing) deals with the level of details to which the security functions are tested by the developer; component \"ATE_DPT.1 Testing: basic design\" provides assurance that the TSF subsystems behave and interact as described in the TOE design and the security architecture description.  
Each SAR component has evaluation evidence assigned, provided by the developer and assessed according to the common evaluation methodology (CEM) [29]. The CC \"SAR components\" correspond to the CEM \"subactivities\", which are divided into evaluation actions. The evaluator investigates the evidence and/or the TOE behavior and assigns verdicts (pass/fail/inconclusive) to the evaluation actions.  
The IT product assurance rises when more effort is devoted to its development and evaluation. This effort depends on the TOE scope (less or more portion of the IT product is evaluated), depth (fewer or more design and implementation details considered), and rigor (a more or less structured and formal approach is applied).  
The Common Criteria methodology (CC, CEM) is described in publications worldwide [34; 35] and in the author's publications [7; 36; 37; 38].
# Experimental Investigation into the Influence of Plasma Technology on Seed Surface Wettability
## 4 Discussion
also observed the positive effects of plasma treatment on the wettability of _Mimosa__Caesalpiniafolia_ seed. The treated seeds showed a contact angle of 43% lower than that of the untreated ones [17]. [PERSON] et al., using diffuse coplanar discharge, changed the wettability of the corn, barley, and wheat surfaces. The contact angles of the water droplet were reduced from 100\({}^{\circ}\) to 20\({}^{\circ}\). According to the authors, changes in contact angles are probably due to oxidation of the seed surface by reactive particles from the plasma [20]. Plasma discharges containing nitrogen (such as those that burn in the air) can result in a more hydrophilic seed surface, manifesting in lowering the contact angle between the water droplet and seed surface. The decrease in contact angles of various seeds with plasma treatments is related to the chemical mechanisms of seed surface modification, making it more hydrophilic. The surface changes are caused by the adsorption of highly reactive radicals such as hydroxyl free radicals (\(\bullet\)OH), ionized molecules of N\({}_{2}\), ozone (O\({}_{3}\)), electrons, and UV radiation. When these species interact with organic surfaces, such as seeds, they promote partial breakage of the polymeric chains that constitute the surface structure, including creating functional groups containing oxygen and nitrogen [28; 29]. Additionally, [PERSON] et al. reported a similar plasma etching effect on inorganic compounds that produce surface defects which can cause certain changes in wettability [30]. It is known that an alteration may occur on the surface of the seed as a result of the incorporation of new functional groups of hydrophilic character or due to an increasing level of surface porosity [31; 32]. Therefore, the functional groups may have modified the seed surface by promoting the appearance of pores or cavities in the seed integument, thus facilitating the entry of water into the seed. Seed pretreatment with plasma may improve the seed surface and water interaction, consequently providing better wettability. Plasma can oxidize the fibers on the surface of the seeds, and it can also produce covalent cross-links of inter fibers. All these things help the seeds to reduce their hydrophobic nature and improve their wettability. Similar mechanisms were also previously suggested in [4].
## 6 Results
### Max PV with Respect to Average Distance  
Maximum injected PV current is analyzed with respect to average distance of PVs in Figure 12. Two cases of variable phase selection are presented: low and high background VU. Despite the slight difference in PV injected power, a similar trend can be seen in both cases. At a small number of PVs (one or two PV plants), the maximum injection is correlated with average distance, whereas at a higher number of PVs (three and four PV plants), the trend is less prominent. As even more PVs are added to the network, their average distance converges to 0.5, measured as relative distance to the farthest-most bus. If the network is almost completely full of PVs, unbalance is cancelled out by fellow PVs, and VU is less of a concern, as it is reflected in high PV injection results.  
Figure 11: Maximum PV injection at different PR values of background VU.  
Figure 12: Maximum PV injection at different PV average distance from the substation.
# Research on Offshore Vessel Trajectory Prediction Based on PSO-CNN-RGRU-Attention
###### Abstract  
In busy offshore waters with high vessel density and intersecting shipping lanes, the risk of collisions and accidents is significantly increased. To address the problem of insufficient feature extraction capability of traditional recurrent neural networks (RNNs) in ship trajectory prediction in busy nearshore areas, this paper proposes a hybrid model based on Particle Swarm Optimization (PSO), Convolutional Neural Networks (CNN), Residual Networks, Attention Mechanism, and Gated Recurrent Units (GRU), named PSO-CNN-RGRU-Attention, for ship trajectory prediction. This study utilizes real Automatic Identification System (AIS) data and applies the PSO algorithm to optimize the model and determine the optimal parameters, using a sliding window method for input and output prediction. The effectiveness and practicality of the model have been fully verified. Experimental results show that, compared to the PSO-CNN-GRU model, the proposed model improves the longitude by 7.8%, 3.4%, and 1.7% in terms of Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE), respectively, and improves the latitude by 48.3%, 62.9%, and 39.2%, respectively. This has significantly contributed to enhancing the safety of ship navigation in the Bohai Strait.  
AIS information; particle swarm optimization algorithm; convolutional neural network; attention mechanism; trajectory prediction +
Footnote †: journal: Applied sciences  
1
Footnote 1: email: {[[https://doi.org/10.3390/app15073625](https://doi.org/10.3390/app15073625)]([https://doi.org/10.3390/app15073625](https://doi.org/10.3390/app15073625))}
## 6 Conclusions and Future Work  
The starting point of this study was to solve the problems of the long-tail distribution and cold start. Through statistical analysis, we found that the long-tail distribution of user activities and item popularity was clear and affected the final recommendation results. Therefore, we propose an aging mechanism that aims to achieve more balanced recommendations for unpopular items or new users by inhibiting user activities and item popularity. We innovatively combined the aging mechanism with the attention mechanism, and introduced it into the model as a weight that had an excellent effect. In contrast, based on multidisciplinary crossover technology, we introduced a famous residual network in the convolutional network into the model, which strengthens the feature crossover and further increases the generalization ability of the model. In the final experiment, we used three different datasets to prove that the ARFM has clear advantages in various respects.  
The significance and contribution of this study are very novel. Based on the structure and characteristics of the dataset, we explore the long tail distribution and cold start problems caused by the dataset, and propose a new aging mechanism to optimize it. Compared with other algorithms or models exploring the network layer structure and mathematical formula, the ARFM model truly achieves the combination of model and dataset. From the final experimental results, ARFM has great potential for practical application and future development. It is hoped that the research results of this paper can provide other researchers with new ideas and make further progress in dataset and model mining.  
In future work, we hope to further optimize the aging mechanism and make it more diverse and suitable for various situations, which will further improve the generalizability of the model and final recommendation accuracy. In addition, we want to conduct experiments on more and larger datasets to further verify whether our model has more advantages when dealing with large datasets, or whether there are potential dangers, which require more time to study datasets, which we find quite challenging. Finally, we will continue to explore the improvement of the ARFM from the model structure to better integrate the functions of all layers and improve the overall benefits of the model.  
Conceptualization, H.Y.; methodology, H.Y.; software, H.Y.; validation, H.Y.; formal analysis, H.Y.; writing--original draft preparation, H.Y.; writing--review and editing, J.Y.; supervision, J.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China under Grant 61971268.  
Not applicable.  
The datasets in this study are available online at UCI Machine Learning Repository: Adult DataSet and MovieLens\(|\)GroupLens.  
We thank the National Natural Science Foundation of China for funding our work, grant number 61971268.  
The authors declare no conflict of interest.
## 3 Results
### DNA-Based Analysis of CMJ Performance
#### 3.2.2 Concentric Mean Power  
The genotype distribution of muscle performance genes in the concentric mean power responder's football players group, when compared with the non-responder's players, was statistically significant for the _MLCK_ c37885C\(>\)A polymorphism (\(p\) = 0.035), showing a higher frequency in the \"heterozygous\" genotype in responder players (CA 21.4%) than the non-responder players (CA 4.3%), since there was a higher frequency of the \"worse\" genotype in the non-responder players (AA 91.3%) than in the responder players (AA 78.6%). No differences were shown in the other muscle performance polymorphisms between the responder and non-responder players (Table 4).  
Figure 1: Individual changes and differences in jump height (**A**), concentric mean force (**B**), and concentric mean power (**C**) between the start of the season (September) and the mid-season (February). Gray area represents the SWC.  
Figure 2: Differences between pre- and mid-season for CMJ-derived metrics as a function of gender.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Symbol** & **Gene** & **Polymorphism** & **dbSNP** & **Genotype** & **Responder** & **Non-Responder** & **_p Value_ \\ \hline _ACE_ & Angiotensin-converting enzyme & I/D & rs4646994 & DD & 9 (64.3\%) & 14 (60.9\%) & \\  & & & ID & 4 (28.6\%) & 8 (34.8\%) & 0.884 \\  & & & II & 1 (7.1\%) & 1 (43.3\%) & \\ \hline _ACTN3_ & Alpha-actinin-3 & c.1729C\(>\)T & rs1815739 & CC & 5 (35.7\%) & 5 (21.7\%) & \\  & & & CT & 4 (28.6\%) & 4 (17.4\%) & 0.332 \\  & & & TT & 5 (35.7\%) & 14 (60.9\%) & \\ \hline _AMPDI_ & Adenosine monophosphate & c.34C\(>\)T & rs17602729 & CC & 10 (71.4\%) & 16 (69.6\%) & \\  & deaminase 1 & c.34C\(>\)T & rs17602729 & CC & 4 (28.6\%) & 6 (26.1\%) & 0.729 \\  & & & TT & 0 (0.0\%) & 1 (4.3\%) & \\ \hline _CKM_ & Muscle-specific creatine kinase & c.*800A\(>\)G & rs8111989 & GG & 0 (0.0\%) & 3 (13.0\%) & \\  & & & GA & 6 (42.9\%) & 6 (26.1\%) & 0.272 \\  & & & AA & 8 (57.1\%) & 14 (60.9\%) & \\ \hline _MLCK_ & Myosin light chain kinase & c.49C\(>\)T & rs2700352 & CC & 9 (64.3\%) & 16 (69.6\%) & \\  & & & CT & 5 (35.7\%) & 7 (30.4\%) & 0.739 \\  & & & TT & 0 (0.0\%) & 0 (0.0\%) & \\ \hline \multirow{2}{*}{_MLCK_} & \multirow{2}{*}{Myosin light chain kinase} & CC & 0 (0.0\%) & 0 (0.0\%) & \\  & & & c.37885C\(>\)A & rs28497577 & CA & 2 (14.3\%) & 2 (8.7\%) & 0.595 \\ \end{tabular}
\end{table}
Table 3: Genotype distribution in responder football players and non-responder football players in jump height for muscle performance polygenic profile.
# YOLO-SE: Improved YOLOv8 for Remote Sensing Object Detection and Recognition
## 1 Introduction
As these single-stage algorithms--for example, YOLO--have matured, they not only outperform two-stage methods in terms of detection speed but also match or surpass them in terms of accuracy. Despite the current state of the art, there is still room for improvement in the detection of objects at multiple scales and small targets. Several object categories in remote sensing display size variations, even within the same category. For instance, ships in ports can range in size from tens of meters to hundreds of meters. Additionally, the height of capture and distance from the target can affect an object's size in the image. Moreover, many small objects are present in aerial images, which are often filtered out in the pooling layers of convolutional neural networks (CNNs) due to their small size, making them challenging to detect and recognize. To tackle these challenges, this paper suggests an improved network model built upon the foundation of YOLOv8. Our approach improves conventional convolution techniques, incorporates state-of-the-art attention mechanisms, and enhances the loss functions. The primary contributions of this paper are as follows:  
(1) A lightweight convolution SEConv was introduced to replace standard convolutions, reducing the network's parameter count and speeding up the detection process. To address multi-scale object detection, the SEF module was proposed, based on SEConv. (2) A novel EMA attention mechanism was introduced and integrated into the network, resulting in the SPPFE module, which enhances the network's feature extraction capabilities and effectively addresses multi-scale object detection challenges. (3) To improve the detection of small objects, an additional prediction head designed for tiny-object detection was added. Furthermore, the original detection head was replaced by a transformer prediction head to capture more global and contextual information. (4) To mitigate the adverse gradients generated by low-quality examples, the Wise-IoU loss function was introduced. (5) In the evaluation on the SIMD dataset, the AP50 reached 86.5%, marking a 2.1% improvement over YOLOv8, outperforming the state-of-the-art model YOLO-HR by 0.91%. Furthermore, we conducted validation on the NWPU VHR-10 dataset, where YOLO-SE achieved 94.9% accuracy, outperforming YOLOv8 by 2.6%. The paper unfolds as follows: Section 2 delves into a comprehensive review of existing work concerning object-detection networks in remote sensing images, with a particular focus on attention mechanisms. Section 3 provides an intricate overview of both the YOLOv8 network and our proposed YOLO-SE network. In Section 4, we present a detailed account of our experiments and conduct a thorough analysis of the results, using both the SIMD dataset and the NWPU VHR-10 dataset. Finally, Section 5 encapsulates our conclusions.