esa-sceva/satcom-chunk-collection huggingface dataset:
# Enconv1d Model Based on Pseudolite System for Long-Tunnel Positioning
## 3 Methodology
### Model Framework
#### 3.2.1 Temporal Feature Extraction Module
This approach enhances information diversity bycapturing different subspaces across various positions. The multi-head attention function is defined as follows:  
\[MultiHead(Q,K,V)=\ Concat(head_{1},\dots,head_{h})W^{O} \tag{7}\]  
where \(head_{i}=Attention(QW_{i}^{Q},KW_{i}^{V},VW_{i}^{V})\), h denotes the number of heads, and \(W^{O}\in\mathbb{R}^{hd_{i}\times m}\). \[FFN(X_{Attention})=\text{ReLU}(XW_{1}+b_{1})W_{2}+b_{2} \tag{8}\]  
The multi-head attention layer and the feed-forward network layer go through residual connectivity and layer normalization, which is designed to reduce gradient vanishing and speed up model convergence. The feed-forward network layer includes two layers of linear transformation and one ReLU activation, and then the residual connection of layer normalization is applied to output the feature values. This study set the number of heads in the multi-head attention module within the feature extraction framework to 2, with two layers. The final feature values are processed through an output layer of linear modules to produce the temporal features \(X_{out}^{Temporal}\).
# Day and Night: Locating the General Practitioner's Panel after Hours
## 3 Results
### Data  
The parameters for nodes, unit supply costs between nodes, and demand at each node were derived from the census data. Census dissemination areas (DAs) were used as the nodes of the model. Dissemination areas are the smallest geographical unit published by Statistics Canada. They are stable geographical areas typically populated by 400 to 700 people. The centroid of each DA was computed and used for possible facility locations. The population of each DA was used as the demand at each node for both time settings (except for the modified case study). The travel distance between nodes was considered the cost between nodes. The distance between nodes was the shortest distance between every pair of nodes in the Nova Scotia road network. That is, the cost between nodes is the shortest distance one could travel via the Nova Scotia road network, not the naive Euclidean distance between points. Distance calculations between each DA are provided by McNamara [14]. Note that this method assumes travel distance within one node to be zero; for example, if a primary care practice is located at node \(i\), patients within node \(i\) are assumed to travel a distance of 0 to this practice.  
The dissemination areas selected to represent Halifax and Cape Breton were those contained within the Halifax Census Metropolitan Area (CMA) and the Cape Breton Census Agglomeration (CA). CMAs and CAs are geographical regions used by the census to indicate which DAs belong to a larger community. They are defined by regions where one or more adjacent municipalities are centred on a population core. For a CA, this means a core consisting of at least 10,000 people. A CMA must have a total population of more than 100,000 with at least 50,000 living within the core. For example, a CMA could have a core of 75,000 residents with 30,000 people living in surrounding municipalities. Images of these dissemination areas within Nova Scotia have been created using ArcMap, shown in Figure 1.  
By using population to model demand in both settings, demand is identical during the day and after hours as the census data do not distinguish population locations by time of day. To test the effect of geographical differences in demand between regular and after-hours settings, a hypothetical setting and a data set were created by modifying the census data. This data set was used to simulate a scenario where a large portion of people  
Figure 1: Nova Scotia with Halifax (center) and Cape Breton (upper-right) Dissemination Areas Highlighted.  
moved from urban areas to rural areas after hours. This may mimic a large population's commute to urban areas from rural areas for work. A cluster of DAs around Sydney (the most densely populated part of Cape Breton) was chosen to represent an urban area, and 25% of the population in these DAs was distributed proportionally among the remaining DAs after hours to create the hypothetical setting. This was performed by rounding the population in urban DAs to 75% and then increasing each remaining DA proportionately so that the total number of people remained the same.
## 3 Simulation Setup and Performance Evaluation
### Packet Loss  
In this scenario, we determine the average packet loss for different numbers of nodes. As we can observe in Figure 11, as the number of the sensor nodes increases then the average packet loss also increases. Based on the simulation results, we confirm that the packet loss is directly proportional to the increased number of sensor nodes. Overall the average packet loss for all simulated QoS real-time routing protocols is relatively similar. However, PT produced a minimum packet loss for different numbers of nodes that ranged from 0.09% to 1.04%, while, other protocols produced 0.09%-1.62% average packet losses. QoSR, MQoSR and PRTR behave poorly with increasing numbers of sensor nodes. The reason of the maximum packet loss for these protocols is not only that they perform localized operations but also by maintaining the per-flow state, which takes a long time to recover in case of packet loss. As a result, nodes are unable to get global fairness.  
Figure 10: Bandwidth consumption at different time periods.
## 5 Conclusions  
1. The method and software for determining the stress-strain state and calculating the bearing capacity of short CFST columns by reducing the three-dimensional problem to the two-dimensional one were developed. The proposed approach leads to significant savings in computational time in comparison with the calculation in the three-dimensional formulation. In the proposed approach, only the displacements of the nodes in the section plane \(xy\) and two additional variables \(\varepsilon_{z}^{0}\) and \(\chi\) act as unknowns.
2. The proposed method was verified by comparing the solution in the elastic formulation with the solution in the LIRA-SAPR finite element software package based on the three-dimensional model. With the same mesh density in the section plane discrepancy in stresses was 0.6% for \(\sigma_{z}\), 1.6% for \(\sigma_{x}\) and 3.1% for \(\sigma_{y}\).
3. The developed method was also tested on the experimental data of [PERSON] and [PERSON] for six eccentrically compressed specimens with different eccentricities of the longitudinal force and diameters. The maximum discrepancy between the theoretical and experimental values of the ultimate load was 1.3%. These results also indicate the possibility of applying [PERSON]'s deformational theory of plasticity of concrete to the calculation of CFST columns.
4. It was found that, with an increase in the eccentricity of the longitudinal force, the effect of concrete work under conditions of a triaxial stress state in CFST columns with a circular cross-section decrease. The increase in bearing capacity due to the work of concrete under conditions of a triaxial stress state for the samples with D = 159 mm was 19% at e/D = 0.065 and only 3% at e/D = 0.26. Thus, the area of effective work for columns of circular cross-section is small eccentricities of the longitudinal force.  
Conceptualization A.C., V.C. and B.Y.; Methodology A.C., A.B. and B.Y.; Software A.C., V.C. and K.K.; Validation A.C. and A.B.; Formal analysis A.C., V.C. and K.K.; Investigation A.C.; Resources A.C., A.B. and B.M.; Data curation A.C., A.B. and V.C.; Writing--original draft preparation A.C. and A.B.; Writing--review and editing, A.C. and A.B.; Visualization A.C., V.C. and K.K.; Supervision, A.C. and B.M.; Project administration, A.C., A.B. and B.M.; Funding acquisition, A.C., A.B. and B.M. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The study do not report any data.  
The authors acknowledge to the administration of the Don State Technical University for support and financial assistance.  
The authors declare no conflict of interest. The funders had no role in the design of the study, in the collection, analyses, or interpretation of data, in the writing of the manuscript, or in the decision to publish the results.
# Routing Attacks Detection in 6 LoWPAN-Based Internet of Things
## 2 Background  
### Routing Protocol for Low-Power and Lossy Networks (RPL)  
The Routing Protocol for Low-Power and Lossy Networks (RPL) is a protocol that is specifically designed for low-power and lossy networks (LLNs) to route messages with minimal power consumption. In this protocol, the network topology is formed as a Destination-Oriented Directed Acyclic Graph (DODAG), with a root node at the top and leaf nodes at the bottom [2].  
Nodes in the network maintain routing tables and communicate with their neighbors to find the best path to the root node. RPL uses a metric known as Expected Transmission Count (ETX) to evaluate the link quality between two nodes. The nodes with better links are ranked higher in the DODAG and are used for routing [2].  
To help save power in the network, RPL includes several features. Nodes can enter sleep mode to conserve energy when they are not actively participating in routing. They can additionally indicate to their neighbors that they are low on energy and should not be used for routing. RPL uses various message types to maintain the network topology and update routing tables. The root node broadcasts a DAG Information Object (DIO) to update the network regarding the current state of the DODAG. Nodes respond with Destination Advertisement Objects (DAOs) to advertise their presence and update their parent node in the DODAG. Lastly, the DODAG Information Solicitation (DIS) message is used to request DIO messages from the root node when a node joins the network or loses its parent [8].  
In Figure 1, the process of forming the Destination-Oriented Directed Acyclic Graph (DODAG) begins with the root node. The structure formed can be in the shape of a tree, and there may be one or more root nodes. The root node starts advertising information by sending out a DAG Information Object (DIO) message. Neighboring nodes receive and process the message to determine whether to join the network or not. This process continues from the root to the parent nodes and leaf nodes in the network. Each node selects a parent node from its neighboring nodes. The root node has a rank of 0, and the ranks of nodes increase based on the Objective Function (OF). To prevent possible packet loss, no loops should be formed, unlike traditional routing [9].  
Routing in RPL begins at the root node and routes are distributed to other protocols connected through the Destination Advertisement Object (DAO) message. RPL is more applicable in IoT where power supply is a regular constraint in low-energy and lossy networks.
###### Abstract  
Online streaming contents are creating greater service uncertainty, as consumers need to experience such contents before making a decision to continue to purchase them. Few studies have investigated the interaction between eWOM (online word-of-mouth) and online streaming content service characteristics with regard to the performance of online streaming contents and explained how this interaction can promote the role of service characteristics in service performance outcomes or remedy service uncertainty attributable to these characteristics. Thus, in order to test the interaction effects, this paper examines the moderating effects of service (webtoon) characteristics (i.e., author experience, genre (drama or fantasy), completion, transfer to paid service, and publication time (Wednesday)) on the relationship between eWOM and certain online streaming contents' service performance measures; in this case, the publication period and content gamification. Based on scrawled data from 154 webtoons published on Naver Webtoon, a multivariate regression analysis with interaction terms showed that author experience and genre interact with the number of reviews to affect gamification. The transfer to a paid service interacts crucially with review ratings and the number of reviews to influence both the publication period and gamification. Online streaming content completion and publication times are factors that interact with review ratings and thus affect the publication period. Service providers need to cope with service uncertainties when attempting to further their online streaming content service by considering the service characteristics as well as customers' responses through eWOM.  
online streaming contents; eWOM; service characteristics; service performance; publication period; gamification likelihood
## 6 Simulation Result
### Evaluation Results of Modifying \(\lambda\) and Evaluation Test on Task Allocation Time  
Since the weight \(\lambda\) in Equation (14) affects the total task execution cost, we evaluate the execution cost of the three schemes under different value of \(\lambda\). From Figure 8, we know that, when changing \(\lambda\), our proposed scheme always achieves the lowest task execution cost among the three schemes. Furthermore, to verify the efficiency of task allocation, we test the task allocation time required to allocate 50, 100, 150, and 200 tasks, respectively, with the results being shown in Figure 9. As can be seen from this figure, the proposed DRL-based scheme takes a short time to complete task assignment, which indicates the efficiency of the proposed DRL model. For example, the total allocation time for assigning 100 offloaded tasks is only 0.057 s.  
Figure 8: Simulation results with diverse \(\lambda\).  
Figure 7: Simulation results with diverse deployed resources. (**a**) Task successful rate under resources, and (**b**) execution cost under resources.
# Real-Time Maintenance Optimization with Industrial Internet of Things
## 4 Results
### Experimental Conclusions  
Based on the comparison of the results of conventional and real-time optimization, the following experimental conclusions can be drawn:  
* In the case of real-time optimization, the same number of maintenance operations can result in a longer production time, as maintenance operations that are not justified by the condition of the system or its resources are not carried out (i.e., the condition of the system or a resource does not reach a threshold that would necessitate a maintenance operation). As a result of real-time optimization, the 20 maintenance cycles examined in the case study allowed for a 12% longer production time (see Table 4);
* In the case of real-time optimization, the income calculated from productivity increases significantly, as constant reliability can be ensured through maintenance operations optimized in real time, thanks to continuous monitoring of the system's condition. As a result of real-time optimization, the revenue calculated from productivity in the system outlined in the case study almost doubled (see Table 4);  
Figure 15: The impact of dynamic, real-time optimized maintenance periods on the specific maintenance cost for a maintenance period, the specific income related to the productivity of the analyzed production system, and the specific profit depending on the maintenance cost and the income based on productivity, in the case of \(r_{min}=0.95\).  
* In the case of real-time optimization, maintenance costs increased by 17% to 775% (see Table 3). This increase is due to the fact that the changing maintenance periods require significant flexibility from the maintenance department, which results in additional costs. Although maintenance costs can increase substantially, the high level of productivity resulting from the stable reliability of the manufacturing system generates revenue that more than covers the increased maintenance expenses. This is supported by the calculated profit;
* In the case of real-time optimization, the profit calculated from productivity-based revenue and maintenance costs increased. In the case study, this increase ranged from 2.5 to 12 times (see Table 4), confirming that real-time maintenance optimization based on IIoT technologies can bring significant economic benefits.
# Resting-State Functional Connectivity in Mathematical Expertise
## 4 Discussion  
In the present study, the evidence of an expertise-dependent brain network was garnered from the disparate functional connectivity between mathematicians and non-mathematicians. The level of integration among brain regions at resting state, in other words, the resting-state functional connectivity, is deeply intertwined with neural architectures that support fundamental aspects of human behavior [14; 55; 56]. Much has been studied about changes to neural networks using resting-state fMRI and task-related fMRI, emphasizing which brain regions establish functionally specialized networks, how functional connectivity changes its strength, and how these changes link to alterations in cognitive functions, emotion, and behavior [26; 57; 58]. Indeed, researchers have suggested that intrinsic connectivity networks acquired from resting-state data are closely linked to individual characteristics such as IQ, personality, and cognitive functions [57]. In line with this, we demonstrated diverging involvements of the frontal-thalamic-temporal connections for mathematicians and the medial-frontal areas to precuneus and the lateral orbital gyrus to thalamus connections for non-mathematicians. Among mathematicians, those who had higher scores in mathematical knowledge showed a weaker connection strength between the left and right caudate nucleus, demonstrating the connections' characteristics associated with mathematical expertise. Moreover, we achieved a maximum classification accuracy of 91.19% when differentiating mathematicians and non-mathematicians using the distinct resting-state functional connectivity features.
# Hashtag Recommendation Methods for Twitter and Sina Weibo: A Review
## 5 Hybrid User-Based Methods
### Social Collaborative Filtering  
Some other researchers assumed that _there is a shared interest of topics between a user and users whom he/she follows_. [PERSON] et al. [67] integrated the candidate hashtags extracted from the content similarity method [33] with candidate hashtags of the most similar users from the set of followees. He also incorporated a temporal weight to rank the hashtags based on the entropy score and the hashtags' age. They found that hashtags with a small entropy and age values are less likely to be reused and therefore should be penalized. [PERSON] et al. [18] put forward _Base Level Learning_ (BLL), a method that recommended the most recent and frequent hashtags placed in the user's tweets and his/her followees. In their experiments, they found that the social factor improves the recommendation performance.  
[PERSON] et al. [22] looked into the hashtag recommendation from a different angle. They built PHAN, a graph-based model of representative users and hashtags. They built their model based on the notion that _when a node user becomes popular, the hashtag represents a topic of interest_. As such, a represented node user was selected if his/her followers exceeds a given threshold. Accordingly, the representative users and hashtags were projected into latent space using Generalized Matrix Factorization (GMF), a Neural Collaborative Filtering method. GMF is a multi-layer neural network that learns user-item relationships for recommendation. They optimized GMF by setting an attention weight that learned the influence of a representative node on a user with respect to a hashtag. With the absence of labels for the representative users, they trained their model based on weak supervision of estimated labels to every representative node.  
Although several studies [18; 22; 67] have highlighted the importance of user relations (behavioural and social), the degree of their influence is not clear. [PERSON] et al. [16; 19; 23] present an overview of the community detection algorithms as techniques used to group like-minded users. The influence of four relations based on the hashtag usage, topics, followees and mentions on the performance of hashtag recommendation over communitieshave been investigated [23]. It was found that the level of social relations affects the performance of hashtag recommendation.
## 3 Results and Discussion
### Identification and Characterization of the Geosmin Synthase Genes of A. _ucrainica_  
Two 943-bp identical fragments were amplified from _A. ucrainica_ strains CHAB 1432 and CHAB 2155 by using primers G2f and G2r. Subsequently, the whole geosmin synthase gene (_geo_ gene) in _A. ucrainica_ was successfully cloned through genome walking-PCR (Figure 2). The _geo_ gene of CHAB 1432 and CHAB 2155 is 2256 bp in length. Only one base pair difference at the 442 bp site (T for 1432 and C for 2155) was found between these two strains. The BLAST search revealed that the _geo_ genes from these two strains were 76.9%, 65.4%, 81.0% and 80.6% identical in DNA sequences and 82.9%, 61.4%, 86.8% and 87.1% identical in deduced amino acid sequences with the well-elucidated geosmin synthase genes, _npummod_ (_N. _punctiforme_ PCC 73102), _geoA1_ (_Phormidium_ sp.), _geoA2_ (_Phormidium_ sp.) and that from _Oscillatoria_ sp., respectively. The high conservation of the geosmin gene in cyanobacteria could provide essential information for the development of molecular monitoring methods of geosmin-producing cyanobacteria. A recent study has reported a quantification method for potential geosmin-producing _Anabaena_ in freshwater on the basis of geosmin gene sequences [20,23].  
Figure 1: Cell growth (**A**) and geosmin productions (**B**) of _A. ucrainica_ CHAB 1432 and 2155 in the test cycle.  
Figure 2: Geosmin synthesis genes of _A. ucrainica_. (**A**) Strain CHAB 1432; (**B**) strain CHAB 2155.  
The functional sites of Geo were identified by CDD search (specific hits with terpene cyclase non-plant C1) and amino acid alignment; these sites include NPUNMOD (_N. punctiforme_ PCC 73102), GeoA1 (_Phormidium_ sp.), GeoA2 (_Phormidium_ sp.), SAV2163 (_Streptomyces avermitilis_ MA-4680) and SCO6073 (_S. coelicolor_ A3 (2)) (Figure S2). In general, the N-terminal domains of all of the analyzed proteins are more conserved than the C-terminal domains and contain two typical Mg\({}^{2+}\)-binding motifs, namely, **DD**HFLE and RNDLFSYQRE sequences (Figure S2) [9,10]. Two less conserved Mg\({}^{2+}\)-binding motifs, namely, **DD**Y(F/Y)(P/H) and **ND**(I/V)(F/V)SY(Q/R)**KE**, were also found in the C-terminus. Interestingly, the N-domains and C-domains of Geo and NPUNMOD were homologous to each other and shared identical motifs, such as the Mg\({}^{2+}\)-binding sites (Figure S3). The DNA sequences of _geo_ and _npummod_ in the N- and C-domains shared 50.9% and 44.2% similarities, respectively.