esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Compared with existing multi-objective scheduling algorithms, which often focus on a single optimization goal such as maximizing task execution rate or resource utilization, our method achieves a more balanced trade-off among execution timing, priority enforcement, and time-slot utilization. This approach--combining established techniques and enhancing them with a time-shifting mechanism--is specifically designed for highly constrained and complex scheduling scenarios and has not been adequately addressed in the existing literature. Therefore, our main contributions are summarized as follows:  
* The integration of time pointer and pulse interleaving algorithms. The time pointer scheduling approach enables the scheduling system to dynamically select and execute the next optimal task based on the current radar's operating status and task queue conditions, typically prioritizing tasks according to their urgency. The pulse interleaving technique further refines the execution gaps between tasks. By inserting additional tasks during the idle periods between radar transmission and signal reception, it increases the density of task execution, thereby optimizing the overall task execution efficiency without compromising high-priority tasks. * The introduction of time shifting constraints. By strictly controlling the deviation between the actual execution time and the expected execution time of tasks, the scheduling system can avoid significant deviations in the task execution time and reduce the risk of task failure or performance degradation caused by excessive time shifts. It also enables the scheduling system to allocate radar resources more reasonably, ensuring that tasks aiming at high-threat targets can be prioritized and executed accurately while not ignoring the needs of other tasks. * Enhancing the on-orbit safety of space stations. The method proposed in this paper augments the radar system's processing capability in multi-task environments, effectively addressing the complexities of the space environment surrounding space stations. It ensures that, when tracking high-speed, high-threat targets, the deviation between predicted and actual positions remains within a controllable range, thereby improving tracking accuracy and reliability and ultimately bolstering the on-orbit safety of space stations.
# Semi-Supervised Medical Image Classification Based on Attention and Intrinsic Features of Samples
## 3 Method
### Channel and Spatial Attention Mechanisms  
As depicted in Figure 2, in this paper, we add the attention mechanism proposed in [30] to the convolutional neural network, which is divided into two modules: channel attention and spatial attention. In terms of channel attention, we define the module as:  
\[\mathrm{M_{c}(F)=BN(W_{1}(W_{0}AvgPool(F)+b_{0})+b_{1})} \tag{3}\]  
where \(F\in\mathrm{R^{H\times W\times C}}\) is the input image, \(H\) and \(W\) are the length and width of the input feature map, and \(C\) is the number of channels of the input feature map.\(W_{0}\in\mathrm{R^{C/f\times C}}\), \(b_{0}\in\mathrm{R^{C/r}}\), \(W_{1}\in\mathrm{R^{C/Cr}}\), \(b_{1}\in\mathrm{R^{C}}\). Firstly, the input characteristic map is reshaped to \(F\in\mathrm{R^{1\times 1\times C}}\) through AvgPool(F), and then through \(W_{0}\) and \(W_{1}\), successively, the calibrated weighting matrix \(\mathrm{M_{c}(F)}\) of each channel is adaptively obtained through extrusion and expansion. The spatial attention module is as follows:  
\[\mathrm{M_{s}(F)}=\left(\hat{t}_{3}^{1\times 1}\left(\hat{t}_{2}^{3\times 3} \left(\hat{t}_{1}^{3\times 3}\left(\hat{t}_{0}^{1\times 1}(F)\right)\right) \right)\right) \tag{4}\]  
Figure 1: Overview of our semi-supervised framework for medical image classification. The teacher’s weight \(\theta^{\prime}\) is updated to the exponential moving average (EMA) of the student’s weight \(\theta\). The objective function of optimizing the student model includes the supervised loss (\(\mathrm{L_{i}}\)) on the marked set and two unsupervised consistency losses (\(\mathrm{L_{if}}\) and \(\mathrm{L_{u}}\)) on the unlabeled set and the marked set, respectively. By minimizing the three losses, the whole framework can fully mine the features of labeled samples and unlabeled ones, thereby accurately classifying them.  
Figure 2: The attention module structure of this paper consists of two modules: channel attention and spatial attention. The two modules run in parallel, and the weighed matrixes of each channel of the sample features and of each image pixel in the channel are obtained spontaneously and then integrated into the original sample features.  
At first, the input sample \(\rm F\in R^{H\times W\times C}\) is subjected to a \(1\times 1\) convolution for dimension reduction, then two \(3\times 3\) cavity convolutions to increase its receptive field, and, finally, a \(1\times 1\) convolution is performed to obtain the weighting matrix \(\rm M_{s}(F)\) of each pixel of each image in the calibrated channel. These are combined to generate the final attention feature map.  
\[\rm M(F)=\sigma(M_{c}(F)+M_{s}(F)) \tag{5}\]  
Since the two dimensions are different, they need to be adjusted to \(\rm R^{H\times W\times C}\) first. The final characteristic diagram is as follows:  
\[\rm F=(F+F\times M(F)) \tag{6}\]  
The intrinsic feature channel hides the basic information of the convolution filter, which is helpful for the network model to learn more useful features for the current task.
## 3 Results
### Comparison Experiment  
In order to verify the effect of the ECA attention mechanism, this study designed some ablation experiments. As the basis of ECA's attention mechanism, the SE attention mechanism is added to Yolov5s for comparison. In theory, the smoke target is mainly a special color feature, and so the spatial attention mechanism has little impact on the network. In order to verify this theory, the convolutional block attention module (CBAM) attention mechanism [23], which integrates the spatial attention mechanism and the channel attention mechanism, is also used as the contrasting network in this study. Table 5 shows the results of the comparison.  
The results show that channel attention significantly improved the detection of straw-burning smoke, especially the ECA attention mechanism. In contrast, the CBAM attention mechanism, which integrates the spatial attention mechanism, has a negative effect on the network. The reason for this situation is that the smoke in the dataset does not appear at a fixed position in the image, and so the effect of CBAM will be worse. Meanwhile, due to the concentration of smoke colors in white, gray, and black, and the input image of the model being an RGB (red, green, blue) channel, channel attention is more suitable for detecting straw-burning smoke. The results show that the depthwise separable convolution can greatly reduce the computational load, but it does not reduce the network performance.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Params** & **Flops** & **Model Size** \\ \hline Faster R-CNN & 28.3 M & 213 G & 114.2 MB \\ Yolov5s & 7.01 M & 15.8 G & 14.4 MB \\ Yolov5s+ECA & 7.22 M & 16.1 G & 14.8 MB \\ Yolov5s+Dconv & 4.56 M & 10.9 G & 9.5 MB \\ Yolo-Light & 4.76 M & 11.2 G & 9.9 MB \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of network model parameters.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Params** & **Flops** & **mAP.5** \\ \hline Yolov5s & 7.01 M & 15.8 G & 0.709 \\ Yolov5s+CBAM & 7.22 M & 16.1 G & 0.695 \\ Yolov5s+SE & 7.21 M & 16.1 G & 0.725 \\ Yolov5s+ECA & 7.21 M & 16.1 G & 0.732 \\ Yolov5s+Dconv & 4.56 M & 10.9 G & 0.708 \\ Yolo-Light & 4.76 M & 11.2 G & 0.750 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Ablation experiment.  
The calculation method of Dconv is point convolution and channel convolution, so for smoke images with an obvious channel information input, parameter reduction can be achieved without reducing accuracy. Finally, Yolo-Light, which combines the ECA module and deep separable convolution, reduces the number of model parameters and increases the mAP.5 value by 4.1% compared with Yolov5s.
## 2 Materials and Methods
Temperature variations affect directly on a FP cavity as they modify the cavity length due to the thermal expansion of the silica. These variations produce a wavelength shift or analogously a FFT phase shift. Once known the variation of temperature through the FBG and the sensitivity of the MOF sensing head to temperature variations it can be directly compensated by subtracting the wavelength shift-FFT phase shift to the MOF's results. A commercial interrogator equipment for FBGs based sensors (SM125, SMARTEC, Lugano, Switzerland) was used to illuminate the network and also to analyze the spectra of the signals reflected from the sensors. The interrogating equipment has a sampling frequency of 1 Hz [33], four different channels, and is remotely controlled through a MATLAB software (MathWorks, Natick, MA, USA) that also executes a FFT real time analysis. This analysis consists of tracking the FFT phase corresponding to the FFT module main component located at 0.8 nm\({}^{-1}\), as shown in in Figure 1b. The main advantage of this technique is the negligible influence of noise (high frequency components) and signal amplitude undesired variations. Moreover, choosing correctly the MOF lengths, several FP sensors could be multiplexed within a single optical interrogators channel through common optical couplers, as it has been demonstrated by authors in [34; 35]. The three sensors where buried in the soil, as Figure 4 illustrates. Figure 3: Calibration of the optical sensor. The optical sensors were placed at the same radial distance from the soil center as the capacitive sensor and buried \(\sim\)10 cm deep (trying to be at the mid depth of the capacitive sensor). In this study, two different types of soil were tested in order to check the performance of the optical sensor in comparison with the capacitive one. The first porous media was a silica sand specified for use in pool filters, water purification, and sports fields. The soil texture was defined as sand [36], 100% sand. The second one was a mix of 50% weight of the silica sand and 50% weight of a contrasting natural soil texture classified as clay loam, 28% sand, 36% silt, and 36% clay [36].
# A Data Loss Recovery Technique Using EMD-BiGRU Algorithm for Structural Health Monitoring
## 3 Experimental Setting  
This section introduces the composition of the data set. In addition, to verify the validity of the EMD-BiGRU, this paper compares it with six other methods, including EMD-GRU, BiGRU, GRU, GBR, RFR, and SVR.
## 3 Findings
### Prognostics and Health Management (PHM) for Unit Systems
#### 3.1.1 Throughput Rate  
The throughput rate is significant for the design and the activity of manufacturing systems. A remarkable quantity of throughput rate related research has been developed to estimate the throughput of manufacturing systems by creating analytical methods with various unreliable machines. [PERSON] et al. [12] characterized the \"throughput rate\" of a manufacturing system, which is equivalent to summing up all the workloads from each unit. Table 6 shows the literature related to degradation of manufacturing systems. In FUS, this performance measure is considered one of the important expected outputs due to its direct relevance for capacity. For example, if a FUS consists of three different machines with different capacities, then the maximum throughput is considered as the summation of all three machines. If the expected demand is less than the capacity of the system, the throughput rate is equal to the demand, otherwise the throughput rate is equal to the total capacity.
## 2 Materials and Methods  
### Promoter Analysis  
The closely spaced RSREs of up to 8 bp (G/A/C)CGCG(C/G/T) were searched in the promoter regions of _2-C-methyl-D-erythriol 2,4-cyclodiphosphate synthase_ (_MECPS_) genes in the following model plants: Arabidopsis thaliana, Solanum lycopeneium, Oryza sativa, Zea mays, Gossypium hirsutum, Glycine max, and _Arabidopsis lyrata_[39; 42; 44]. The 2 kb long sequences of the promoter regions and the entire 5'UTR were retrieved from the PlantPAN3.0 database [44]. Although the promoter region in _A. thaliana_ is approximately 0.5-1.0 kb long, the most biologically reliable _cis_-active elements are localized within the proximal promoter, approximately up to 300 bp upstream and 200 bp downstream from the transcription start site [45; 46; 47]. Therefore, the searches were concentrated on the proximal promoter and 5'UTR regions. In the second phase, the same closely spaced RSREs were searched in the proximal promoter and 5'UTR regions of other MEP pathway genes, i.e., _1-deoxy-D-xylulose-5-phosphate synthase (DXS), 1-deoxy-D-xylulose 5-phosphate reductase_ (_DXR_), 2-C-methyl-D-erythriol 4-phosphate cytidyltransferase (CMS), 4-(cytidine 5'-diphospho)-2-C-methyl-D-erythriol kinase (CMK), 4-hydroxy-3-methylbut-2-enyl diphosphate synthase (HDS), and 4-hydroxy-3-methylbut-2-enyl diphosphate reductase (HDR)_[6; 7; 8; 9]. The promoter and 5'UTR sequences of the model plants, _Arabidopsis thaliana_, Solanum lycopeneium, Oryza sativa, Zea mays, Gossypium hirsutum, Glycine max, and _Arabidopsis lyrata_, were retrieved from the PlantPAN3.0 database [44].
# WordMap: Text Mining Application of Enhanced Corpus Segmentation and Semantic Topic Recognition
###### Abstract  
This study presents WordMap, an integrated text mining application developed to enhance the efficiency and usability of text analysis over a network. As unstructured text data continues to grow across domains, effective tools for segmentation and topic modeling have become increasingly essential for extracting insightful information. However, most existing solutions depend on multiple disconnected tools, and these often compromise workflow efficiency and user experience. Unlike traditional tools, WordMap combines corpus segmentation, topic modeling, and result visualization into a unified workflow for both Chinese and English languages, thereby reducing workflow fragmentation and lowering the user threshold. To assess usability and user acceptance, this research adopts the Technology Acceptance Model (TAM). WordMap employs PKUSEG and NLTK for bilingual corpus segmentation, utilizes BERTopic for dynamic topic modeling, and integrates interactive visualization to enable intuitive analysis. The PLS-SEM result shows that the perceived ease of use (PEOU) has a significant impact on both perceived usefulness (PU) and user attitude (ATT), while ATT strongly predicts behavioral intention (BI) (\(\beta\) = 0.674, \(p\) < 0.001). The results indicate that integrating core text mining processes into a user-centered design significantly boosts user satisfaction and adoption. By combining key processes and empirically validating user perceptions, the proposed framework facilitates the development of efficient and accessible text mining tools. It offers both theoretical and practical insights for future advancement and deployment in the field of text mining.  
text mining; corpus segmentation; topic recognition; topic modeling; user acceptance; TAM; PLS-SEM Article
## 3 Results and Discussion
The monthly PV production of hypothetical PV systems for both capacities is shown in Figure 5. As expected, most electricity production occurs between May and August. Figure 4: Prospects of the national electricity demand balance, with projected PV production subtracted from the projected consumption for each corresponding year. Annual production is the sum of production over the whole year. Due to module degradation, it is necessary to take into account the decline in annual production over a longer period of time. Considering an annual degradation of 0.05%, the annual production for both PV system capacities is shown in Figure 6  
For all calculations, regardless of the type of investment, we used the values of the variables listed in Table 2. The values of the variables that vary by scenario are documented in Table 3. The prices were set according to historical data, and the aluminium price includes the \"premium\" of the smelter. In summer, on sunny days, there may be a negative price for electricity surplus a few hours a day, but in general, we estimate that the average price will be between EUR 10/MWh and EUR 20/MWh. \begin{table}
\begin{tabular}{l l l} \hline \hline
**Variable** & **Value** & **Unit** \\ \hline \(E_{AL}\) & 14 & MWh \\ \(r\) & 0.00055 & / \\ \(g\) & 196.39 & Wp/m\({}^{2}\) \\ \(o\) & 0.79 & EUR/Wp \\ \(O\) & 1.39 & EUR/m\({}^{2}\) \\ \(e\) & 90 & \% \\ \(b\) & 0.43 & MEUR/MWh \\ \(deg_{b}\) & 3 & \% \\ \(M\) & 1.5 & \% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Variable values used for economic calculations. Figure 5: Monthly electricity production of the hypothetical PV systems with capacities of 60 MWp (**a**) and 90 MWp (**b**). Figure 6: Annual electricity production of the hypothetical PV systems with capacities of 60 MWp (**a**) and 90 MWp (**b**). With the input data prepared, we analyse the economic aspects for all scenarios in the following subsections.
## 2 Existing Approaches
### Game-Based Security Studies  
There are a number of works of literature where game theory concept is harnessed specifically to secure the wireless network. Game theory is used to build a strategic mathematical model to understand the interaction among players involved in the communication process. This involves implementing game theory to identify and resist vulnerable conditions during communication [37; 38; 39; 40]. There has been some interesting research using game theory which offers potential insight into its capability to model intrusive interactions among communicating nodes [41]. The work done by [PERSON] et al. [42] developed a game model of multiple layers on intrusion determination in ad hoc networks, which they claimed was capable of identifying various forms of attacks. [PERSON] et al. [43] developed a tree-based security model that considers communication and security as two essential attributes of the Bayesian Nash Equilibrium game. The work of [PERSON] et al. [44] presents a game-based mechanism to resist byzantine attacks, which takes into consideration the uncertainty of identifying the attackers. The implementation of the Stackelberg game model to fight jamming attacks is seen in the work of [PERSON] et al. [45]. Their model offers a distinctive role for standard transmitters and attackers, while it also implements a genetic algorithm for the optimal usage of resources. [PERSON] et al. [46] present a sophisticated game model using Bayesian and Stackelberg games to resist intelligence jammers. This strategy also determines the rate of decline of transmission and eavesdropping. A unique adoption of the Stackelberg game and learning mechanism is seen in the work of [PERSON] et al. [47]. The model accomplishes an equilibrium stage using a log-linear learning scheme to define its access policies. Furthermore, they experimented with using game theory to resist denial-of-service attacks in vehicular networks [48], combating network layer attacks [49], and improving trust in IoT [50].
## 4 Results
### Selection of Backbone Network  
The detection and segmentation of mature green tomatoes is trained based on different backbone networks, i.e., ResNet50, ResNet50-FPN, ResNet101-FPN, ResNeXt101-vd-FPN and SENet154-vd-FPN. The values of performance evaluation indexes with different backbone networks are shown in Table 1. Compared with other backbone networks, the reasoning time of ResNet50, ResNeXt101-FPN and SENet154-vd-FPN is longer (smaller FPS value), so reasoning and prediction will consume more time in the actual deployment, which cannot meet the real-time requirements of image detection and segmentation. ResNet50-FPN performs best for this customized mature green tomato dataset, therefore, which is selected as the backbone network of Mask R-CNN in this study.
# A Bidirectional Trust Model for Service Delegation in Social Internet of Things
## 4 Simulation and Results  
In order to verify the validity of the subjective logic-based trust model proposed in this section, this study conducts experiments based on the Netlogo experimental platform [37]. NetLogo is an agent-based programming language, which is useful to simulate the interaction among objects and monitor the state changes in a simulative SIoT environment. The construction of the experimental platform is based on our previous work [38]. The trust evaluation mechanism module and service delegation module are adjusted based on the aforementioned bidirectional model. The experiments are divided into the following parts: First, after the interactive experiment, the results of the bidirectional trust evaluation of SPs to SR and SR to SPs are observed to test the effectiveness of subjective logic in the process of trust evaluation. On this basis, the impact of similarity of the services/tasks and positive evaluation rates on trust evaluation results are analyzed. Then, the influence of the number of recommenders on the compositive trust evaluation results is analyzed, and finally the benefits of SR and the changes in the number of responding SPs are measured.  
This study defines the rate of positive evidence (RPE) as the proportion of the number of simulated service results that are rated as \"positive--that is, satisfied\" in the total number of service evaluations. Similarity, the rate of task similarity (RTS) is the similarity of the attributes among the services. For example, when the similarity is 40%, it means that 40% attributes of randomly generated services in the network are consistent. In this experiment, a total of 110 virtual nodes are deployed for service interaction, of which 10 nodes are employed as SRs and 100 nodes are employed as SPs. At the same time, the above virtual nodes will also serve as intermediate nodes in the process of trust evaluation to provide recommendations.
# Hybrid Trust Model for Node-Centric Misbehavior Detection in Dynamic Behavior-Homogeneous Clusters
## 2 Related Work  
Vehicular ad hoc networks (VANETs) aim to enhance traffic management and minimize road accidents by offering various safety applications [12]. VANETs include connected vehicles, natural vehicles, Roadside Units (RSUs), Global Navigation Satellite Systems (GNSS), and cloud platforms. In a vehicle network, vehicular communications comprise Vehicle-to-Vehicle (V2V), Vehicle-to-Infrastructure (V2I), Vehicle-to-Pedestrian (V2P), and Vehicle-to-Network (V2N) communications, collectively referred to as Vehicle-to-Everything (V2X) communications [13], as Figure 1 shown.