esa-sceva/satcom-chunk-collection huggingface dataset:
# Data Fusion-Based Structural Damage Identification Approach Integrating Fractal and RCPN
## 4 Damage Identification of ASCE Benchmark Structure [41]
### Comparison and Discussion  
It can be seen from the comparison of structural damage identification results in Tables 2-4, with the increase of added noise level, the accuracy of damage identification of the single RCPN classifier model, feature-level fusion model and decision-level fusion model decreases. When the noise levels were 30%, 50% and 70%, the total average IA of the single RCPN classifier was 91.6%, 87.25% and 78.3%, respectively, and the total average IA of the feature level fusion model was 95.2%, 88.7% and 81.0%, respectively. The total average IA of decision-level fusion model is 96.9%, 90.7% and 84.9%, respectively. The comparison of damage identification accuracy is shown in Figure 6.  
It can be clearly seen from Figure 6 that for structural damage identification based on correlation dimension, the IA of decision-level fusion model is better than that of feature-level fusion model, and feature-level fusion model is better than the single RCPN model. It can also be seen from Figure 6 that when the noise levels are 30%, 50% and 70%, the total average IA of the feature-level fusion model is 3.6%, 1.45% and 2.7% higher than that of the single RCPN model, respectively. The total average IA of the decision-level fusion model is increased by 1.7%, 2% and 3.9% compared with the feature-level fusion model, and by 5.3%, 3.45% and 6.6% compared with the single RCPN model, respectively. From these comparison results, it can be seen that with the increase of noise level, the total average IA of the feature-level and decision-level fusion model is significantly improved compared with the single RCPN model, indicating that the proposed feature-level fusion model and decision-level fusion model had better noise resistance.  
The comparison between Tables 3 and 4 shows that, when the noise level is 70%, the highest and lowest identification accuracy of the decision-level fusion model is 85.75% and 84%, respectively, while that of the corresponding feature-level fusion model is 81.75% and 80.25%, respectively. Compared with the feature-level fusion model, the average IA of the decision-level fusion model is increased by 5.5% at the highest and 2.25% at the lowest, indicating that the decision-level fusion model has better noise resistance than the feature-level fusion model. Moreover, it can also be seen from Tables 3 and 4, for different damage indexes,  
Figure 6: Comparison of damage identification accuracy of three models.  
the IA at the same noise level has little difference, which indicates that the two structural damage models are insensitive to damage feature vectors and have good robustness.
## 4 Discussion of Results
### Influence of Filler Contents and Binder Types
#### 4.2.2 Analysis of Aging Index  
To exhibit the influence of aging on the mechanical properties of asphalt mastics, an aging index was proposed based on the changing ratio of the G* at 15 \({}^{\circ}\)C and 10 Hz from un-aging to 8 h aging, which was in accordance with Equation (6).  
\[Aging\ index=\frac{G_{8h}^{*}-G_{0}^{*}}{G_{0}^{*}} \tag{6}\]  
As can be seen in Figure 8, both filler content and binder types could affect the aging index dramatically. In Figure 8a, the aging index increased with the increasing filler content in a quadratic function, and the R\({}^{2}\) could reach 0.99. Since the oxidation of mastics is a diffusion mechanism [25; 26], the effect of filler content on the aging index may come from the different binder film distributions  
Figure 6: Master-curve of the complex modulus of mastics with different compositions before aging: (a) different filler contents; (b) different binders.  
Figure 7: Fatigue parameters (G*sin \(\delta\) @ 15 \({}^{\circ}\)C and 10 Hz) of different mastics with varying filler contents and binders before and after aging.  
caused by different volume of fillers inside the binder. Additionally, as shown in Figure 8b, the aging index of the mastic with unmodified binder was larger than mastics with SBS modified binder, so the modification of the binder can also influence the aging properties of mastics.
# Train Scheduling with Deep Q-Network: A Feasibility Test
## 5 Experiments
### Results  
We train the neural network with \(19,264\) episodes and it takes \(29,878.40\) s to finish training. We first evaluate the learned neural network with 50 random instances consisting of only two trains: one local train and one express train. Table 2 demonstrates the result. The columns of the table indicate the instance number (#), the optimal objective value (MIP), the objective value from the learned agent (DQN), and optimality gap (Gap), respectively. The optimal gap is computed by \((\text{DQN}-\text{MIP})/\text{MIP}\times 100\).  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \# & MIP & DQN & Gap (\%) & \# & MIP & DQN & Gap (\%) \\ \hline  
\hline \hline \end{tabular}
\end{table}
Table 2: Comparison on 50 2-train instances.  
As observed in Table 2, the learned policy successfully finds optimal schedules for all 50 instances.  
Table 3 demonstrates the result tested on 50 random instances consisting of 3 trains, that is, 2 local trains. The structure of Table 3 is same as that of Table 2.  
Among 50 instances, the learned policy succeeds at finding the optimal solutions for 36 instances. The gaps for the instances for which the learned policy fails to give optimal solutions are relatively low (within 10%), except for instances #1, #23, #29, and #44. However, the schedules obtained by the learned policy turn out to be largely improved from their initial schedules of which objective values are \(28,112,28,412,28,712\), and \(28,412\), respectively. Furthermore, the sequences of overtaking stations determined by two schedules are quite similar, for example, \((6,15)\) for optimal schedule and \((4,15)\) for the schedule by the learned policy.  
Finally, we tested on random instances consisting of 4 trains, that is, three local trains. The structure of Table 4 is again same as the previous two.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \# & **MIP** & **DQN** & **Gap (\%)** & **\#** & **MIP** & **DQN** & **Gap (\%)** \\ \hline  
& 7328 & 0.00 & 50 & 8253 & 8550 & 3.60 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison on 50 3-train instances.  
Table 4 shows that the performance of the learned policy is quite poor compared with the previous two results. The average gap over 50 instances is 14.51% (0.00% and 2.87% for the previous two results, respectively). Four-train instances are more difficult than the 2- and 3-train instances. In particular, during the training phase of the neural network, the agent has little experiences on the cases of 2 and more overtaking stations. Indeed, by investigating the experience memory which stores the experience the agent used during training, we found that the proportions of the experiences considering 0, 1, 2, and 3 overtaking stations are about 10%, 45% 25%, and 20%, respectively. Therefore, there exists a severe imbalance of experiences if a simple \(\epsilon\)-greedy exploration strategy is adopted. We conjectured that this phenomena can be avoided when we design more sophisticated strategies for the exploration during training.
# Energy-Efficient LoRa Routing for Smart Grids
## 3 Proposed IoT-Based Communication in Smart Grid Environment
### LoRa Gateway  
A gateway with a LoRa connection may aid in end node communication. The data are stored and maintainedby the users in the gateway, which serves as a bridge between the end nodes and the network servers. There was a concentrator board that can receive and forward the LoRa packets to the server in the LoRa gateway. For control purposes, reverse communication from the server to the nodes was allowed by it. It could gather all the data received from the nodes even though the gateway had no data processing capabilities. Then, they were relayed to the server for processing.
# Joint Resource Allocation for Multiuser Opportunistic Beamforming Systems with OFDM-NOMA
## 4 Problem Solution
### Convergence and Complexity  
Convergence: Since the proposed joint iterative algorithm includes two suboptimal respects, i.e., power allocation (25) and subcarrier scheduling and user pairing (31) issues, the convergence of Algorithm 2 is determined by both the issues. For power allocation issue, \(\mathbb{G}\
eq\emptyset\) and (30) has a solution, thus the power allocation scheme is convergent. The number of exhaustive searches is finite, which leads to the convergence of subcarrier scheduling and user pairing problem. Considering both the convergences of suboptimal issues, the proposed Algorithm 2 is convergent.  
Complexity: As with the analyses of convergence, the complexity of Algorithm 2 can be achieved through the complexities of the two suboptimal issues. The complexity of power allocation is \(\mathcal{O}\big{(}\lambda_{p}\big{)}\), where \(\lambda_{p}\) represents the total complexity to complete once derivation (28) and numerical comparison (29). The complexity of subcarrier scheduling and user pairing issue is determined by the number of exhaustive searches and equals to \(\mathcal{O}(N_{c}\times U\times(U-1))\). Moreover, the computational complexity to generate OBF weight is \(\mathcal{O}(N_{T}\times U)\). Here, the complexity of Algorithm 2 is expressed as \(\mathcal{O}(\lambda_{p}\times N_{c}\times N_{T}\times U^{3})\).  
Comparison with other existing schemes: There are three kinds of traditional beamforming schemes to deal with the limited feedback information, i.e., Grassmannian subspace packing (GSP), genetic algorithm (GA) and vector quantization (VQ). According to [27; 28; 29], the computational complexities to obtain beamforming weights of GSP, GA and VQ are \(\mathcal{O}(N_{T}^{2}\times U^{2}\times L_{\text{gsp}})\), \(\mathcal{O}(N_{T}^{2}\times U^{2}\times I_{\text{ga}})\) and \(\mathcal{O}(N_{T}^{2}\times U)\), respectively. \(L_{\text{gsp}}\) and \(I_{\text{ga}}\) present the dimension of subspace and the number of iterations, respectively. Therefore, the total computational complexities of GSP, GA and VQ are \(\mathcal{O}(\lambda_{p}\times N_{c}\times N_{T}^{2}\times U^{4}\times L_{ \text{gsp}})\), \(\mathcal{O}(\lambda_{p}\times N_{c}\times N_{T}^{2}\times U^{4}\times I_{ \text{ga}})\) and \(\mathcal{O}(\lambda_{p}\times N_{c}\times N_{T}^{2}\times U^{3})\), respectively. Moreover, an ideal CSI scenario is considered, where coherent beamforming (CBF) scheme [30] is applied to transmit signals. Based on [31], the computational complexity of CBF is \(\mathcal{O}(\lambda_{p}\times N_{c}\times N_{T}^{3}\times U^{5})\). Comparing the proposed scheme with GSP, GA, VQ and CBF schemes, it is found that the proposed scheme achieves the lowest computational complexity.
## 2 Datasets
### High Wind Speed Measurements from SFMR  
Due to extreme weather, it is extremely difficult to obtain wind speed during tropical cyclones, resulting in a scarcity of measured data. The National Oceanic and Atmospheric Administration (NOAA)/Hurricane Research Division's SFMR has made great contributions to monitoring tropical cyclone wind speed by airborne remote sensing instruments for estimating the ocean surface brightness temperature at six frequencies between 4.6 and 7.2 GHz [42; 43; 44]. With the continuous optimization of the inversion model, the accuracy of its high wind speed measurement is also gradually improving, within \(\sim\)3.9 m/s in terms of RMSE [45]. In this paper, the independent SFMR high wind speed measurements are used as the true value to verify the performance and generalization ability of the machine learning models.
# Robotic Button Mushroom Harvesting Systems: A Review of Design, Mechanism, and Future Directions
## 3 Mechanical System
### Collection System  
After the mushrooms are detached by the end-effectors, they are typically moved to a cutter area before being dropped at the collection site. In fruit and vegetable harvesting, cutters are often integrated with the end-effectors as a single unit since the end-effector alone might not fully detach the produce [54]. However, for button mushrooms, end-effectors are usually designed to pick either just the mushroom cap or the cap along with part of the stem. Since the stem can be contaminated with soil from the bed, it often needs to be trimmed. This trimmed stem can still be utilized for various applications [15; 16].  
Although there is limited literature on the detailed analysis of cutters or collection systems specifically for button mushrooms, some designs incorporate these mechanisms (Figure 4). For example, [PERSON] et al. experimented with different blade shapes and cutting mechanisms, including horizontal blades and circular rotating blades, though the cut profiles were not always clean [26]. [PERSON] et al. integrated a planar knife blade with a pneumatic cylinder and photoelectric sensor for mushroom detection, positioning the cutter on a Cartesian frame with the end-effector [39]. However, the performance of the cutter is not discussed in the published work.  
In commercial harvesting, integrated collection units are more common than separate cutting units. On-shelf collection mechanisms, like the rotating platform used by Mycionics or conveyor belts on shelves, allow manipulators easier access and reduce travel time [38; 58]. The rotating disc and some vertical collectors had some slots where the picked mushroom was dropped. These slots ensure the secure holding of mushrooms during transportation to the other units. The rotating platform also requires additional mechanisms to transfer the mushrooms from place to place. Off-the-shelf collection units--such as vertical collectors like baskets or integrated systems that handle cutting and post-harvesting--off  
Figure 3: Different types of end-effectors are used in picking button mushrooms: (**a**) vacuum suction (reprinted from ref. [38]); (**b**) a combination of suction cups and gripper in an end-effector (reprinted from ref. [41]); (**c**) soft gripper (reprinted from ref. [57]); (**d**) closed grasper (reprinted from ref. [59]); and (**e**) three-finger hybrid gripper (reprinted from ref. [3]).  
more flexibility by being accessible to all layers of multi-storied shelves [40; 43; 44; 51; 45]. Some systems, like the one developed by [PERSON] et al., even include sorting mechanisms with integrated cameras for size-based sorting and collection [61]. These off-the-shelf mechanisms can be moved between locations, often requiring mobile platforms or vehicles for transport.
# Dynamic Behavior of Track Bridge on Serviced Long-Span Road/Railway Bridge
## 2 Finite Element Analysis
### Modeling  
The displacement of the structure imparted by the fixed load, and the initial tension of the suspension bridge, directly affect the stiffness of the suspension bridge. Therefore, before analyzing a live load, we conducted an initial equilibrium state analysis to match the geometric shape and tension of the cable with the design conditions.  
We conducted a three dimensional (3D) numerical analysis using LUSAS 15.2 [10], a nonlinear, general-purpose finite element analysis program that includes cable elements. To ensure the consistency of the analysis, the structure was idealized based on the structural analysis.  
Furthermore, to reflect the structural form of the Yeongjong Bridge direct fixation track structure to the greatest extent possible, the track girders and vertical stiffeners were composed of 3D shell elements: The rail, crossbeam and bracing were modeled as beam elements, and the rail pads were modeled as spring elements (Figure 3), and these models were based on the design drawings.  
The section specifications used in the structural idealization process of the Yeongjong Bridge suspension bridge were calculated based on the completed drawings [8]. The suspension bridge superstructure comprises a truss structure and a box structure. Truss members were applied as a frame structure, and the box girder structure including the upper chord was applied to the structural model based on the original design concept.  
Figure 3: Yeongjong Bridge and track modeling. (**a**) Full model; (**b**) Suspension bridge; (**c**) Truss bridge; (**d**) Yeongjong Bridge track girder model.  
Figure 2: Yeongjong Bridge direct fixation track structure cross section. (**a**) Front view; (**b**) Side View.  
In this study, the fixed loads applied on the suspension bridge were 530.824 kN/m for the center span, 539.513 kN/m for the sidewall and 509.975 kN/m for the truss bridge.  
For the live vehicle load, DL24 loads were applied to eight lanes on the upper road and six lanes on the lower road. In the numerical analysis, by considering the vehicle load as a fixed load, only the influence of the increase in the Korean Train Express (KTX) railway vehicle load was analyzed.  
For the train load, we attempted to reflect the eccentricity of the actual load of the KTX with reference to the railway bridge dynamic stability and dynamic design standard, and applied the load to a single line. The train speed was increased from 20 km/h to 200 km/h in increments of 20 km/h, and the effects on the bridge were analyzed. The actual operating train load was expressed as a time history function over each speed increment. Furthermore, a straight-line running load model of the moving load passing over the elements of the rail (divided into regular intervals) was applied in the analysis.
# Self-Training with Entropy-Based Mixup for Low-Resource Chest X-ray Classification
## 4 Experiments  
### Datasets and Base Model  
Experiments were conducted to verify the validity of the proposed method using the ChestX-ray14 dataset published by the National Institutes of Health (NIH) [31]. The dataset consists of 112,120 X-ray images of 30,805 patients with multiple labels for 14 diseases (atelectasis, cardiomegaly, consolidation, edema, pleural effusion, emphysema, fibrosis, hernia, infiltration, mass, nodule, pleural thickening, pneumonia, and pneumothorax). Among the data, 86,524 data were defined as training data, and the remaining data were used for validation and testing.  
We used 5% of the training data for the first experiment regarding entropy-based Mixup, and then 15,000 training, 5000 validation, and 5000 test data for the second experiment regarding self-training. To establish an experimental environment considering the actual field, the ratio of data for each class in the training, validation, and test datasets were configured with the same ratio for each disease of the original data. Figure 5 shows the distribution of 15,000 training data by classes. The difference in data distribution for infiltration (2366, 13.02%) and hernia (27, 0.15%) was 87 times; compared with no finding (8747, 48.15%), the proportion of the hernia class was only 1/323. For reference, the ChestX-ray14 dataset contained 86,524 training data and 25,596 test data samples. Among them, there  
Figure 4: Oversampling by Mixup: (**a**) oversampling equally to the largest class (_Mixup to same size_); (**b**) oversampling every class by same percentage (_Mixup to same ratio_).  
were 227 samples that belonged to the hernia class. All of the remaining 90,000 data were assumed to be unlabeled data.  
ResNet-50 [32] was used for both the teacher model and the student model, and Adam and BCELoss were used as the optimization algorithms for multilabel problems. As a preprocessing step, 14 diseases were set in the same order as in the previous study, and a 14-dimensional vector was generated by indicating the disease found in the sample image as 1. In the case of no finding, it became a vector in which all elements were 0. Multiple binary classification was performed by setting 14 nodes in the last layer of the ResNet-50 model and applying a sigmoid function.
###### Abstract  
Terpenes are pivotal for plant growth, development, and adaptation to environmental stresses. With the advent of extensive genomic data and sophisticated bioinformatics tools, new insights into the evolutionary dynamics and functional diversification of terpene synthases (TPSs) have emerged. Despite genome-wide identifications of the TPS family in certain species, comprehensive cross-species analyses remain scarce. In this study, we conducted a genome-wide identification and subgroup classification of TPS families across 115 angiosperms with available genomic sequences. Our phylogenomic synteny network analysis elucidated the complex evolutionary history of TPS genes, revealing notable expansions and contractions among subgroups. Specifically, TPS-a showed significant expansion, while TPS-b was variably lost in some Poaceae, indicating adaptive responses. TPS-c maintained considerable conservation across species, whereas TPS-e/f diverged into distinct evolutionary trajectories despite functional overlap, with TPS-e further splitting into two angiosperm-specific clades. The TPS-g subgroup displayed lineage-restricted distribution, primarily in super-rosids and monocots. Notably, TPS-d and TPS-h subgroups were absent in angiosperms. Employing _Rosa chinensis_ as a case study, we identified RcTPS23, a conserved bifunctional terpene synthase, highlighting the utility of cross-species synteny data in functional prediction. This comprehensive analysis elucidates the phylogenetic and functional landscape of TPS subgroups in angiosperms, providing a robust framework for predicting TPS function and guiding further functional investigations.  
genome wide identification; synteny analysis and networks; TPS; super rosids; bifunctional gene
## 5 Conclusions  
Complete geometric errors of multi-axis machine tools are measured by the coupling and decoupling method proposed in this paper. Based on the HTM, the transfer law of geometric errors is researched. The correctness of the coupling and decoupling measurement method is proved via the comparison result with the traditional method. The geometrical error transfer modeling can be referred to other similar researches. The calculation result of integrated geometric errors considering the small angle errors shows that:  
(1) Considering complete geometric errors, actual integrated geometric errors of multi-axis machine tools investigated have a higher level (1 mm), about 10 times, than the result of the traditional measurement method.  
Figure 15: Integrated geometric errors of machining points measured by the ball bar test (2D view).  
(2) The impact of items of small geometric angle errors is more significant than that of items of geometric displacement errors.  
(3) The larger the sizes of the machine tool structures are, the larger the integrated geometric errors are.  
**Author Contributions:** Conceptualization, H.W. and S.Z.; methodology, H.W.; validation, H.W., S.Z. and Y.L.; formal analysis, H.W.; investigation, S.Z.; resources, Y.R.; data curation, Y.L.; writing--original draft preparation, H.W.; writing--review and editing, Y.R., S.Z. and H.W.; project administration, Y.R.; funding acquisition, Y.R. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the National Natural Science Foundation, China, grant number 51835001; 51705048 and the National Major Scientific and Technological Special Project for \"High-grade CNC and Basic Manufacturing Equipment\", China, grant number 2016 ZX04004-005; 2018 ZX04032-001; 2019 ZX04005-001.  
**Acknowledgments:** The authors gratefully acknowledge the valuable comments and suggestions given by the anonymous referees and editor.  
**Conflicts of Interest:** The authors declare no conflict of interest.