esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
Chevron-braced frames have been widely investigated by the scientific community. Studies on these structures were intended either to (i) examine the cyclic response of the brace; (ii) assess the dynamic response of braced structures designed according to seismic codes; or (iii) propose alternative design procedures.  
Regarding the first issue, experimental activities were carried on braces with a different slenderness and cross-section shape [1, 2, 3, 4] to evaluate their cyclic behaviour and axial displacement capacity [5]. In this regard, the displacement capacity is expressed as a function of the slenderness of the brace, cross-section aspect ratio and width-to-thickness ratio. Conversely, the displacement demand to be compared to the displacement capacity has been expressed as a function of either the cumulated tensile ductility demand of the brace [6], the cumulated ductility demand in tension and compression [7] or the sum of the maximum ductility demands in tension and compression [8]. A different approach was followed in [9, 10], where the displacement capacity is predicted considering the low-cycle fatigue in the numerical model of the brace.  
Regarding the second issue, some studies have been carried out to compare the effectiveness of the design procedures included in the European, North American and Canadian seismic codes in ensuring ductile collapse mechanisms [11, 12]. Other studies have been focused instead on the evaluation of the impact of design provisions on the response of chevron-braced frames [13, 14, 15]. In particular, these studies pointed out the need of ensuring an adequate flexural stiffness of the beam of the braced frame and that the out-of-plane buckling of columns often occurs when analysing the response of structures designed according to the European seismic code [16].  
Regarding the third issue, authors have proposed procedures for the design of chevron-braced frames. However, the majority of these design procedures can be attributed to three main approaches. In the following section, the leading ideas of the different design approaches/methods are briefly described, mainly focusing on the design of braces, beams and columns. Based on an analysis of features and the shortcomings of these approaches, a new design method is formulated, proposed and investigated in the paper.
# A Deep Reinforcement Learning Floorplanning Algorithm Based on Sequence Pairs +
## 5 Experimental Results
### Experimental Results and Analysis
#### 5.2.2 Experimental Results of MCNC and GSRC Test Sets with Obstacles  
The optimization of area and wirelength for MCNC benchmark circuits was conducted, and the experimental results are presented in Table 6. Since the fixed module placement constraint was employed, the proposed algorithm is only compared with the simulated annealing algorithm in this context. It can be observed from the table that, for all five test circuits, the proposed algorithm achieves a smaller floorplan area, DS, and wirelength than the simulated annealing algorithm. The proposed algorithm demonstrates an average improvement of 9.2% in wirelength and 3.4% in DS compared to the simulated annealing algorithm. Therefore, regarding the MCNC benchmark circuits under the fixed placement constraint of three modules, the proposed algorithm exhibits more significant advantages in optimizing the floorplan area and wirelength compared to the simulated annealing algorithm.  
Table 7 compares the experimental results of three GSRC benchmark circuits, under the constraint of fixed module placement. It can be observed that the proposed algorithm in this paper outperforms the simulated annealing algorithm in terms of planar floorplan area, wirelength, and DS. Compared to the simulated annealing algorithm, the proposed algorithm achieves an average improvement of 11.2% in wirelength and 8.5% in DS. From Tables 5 and 6, it can be seen that, as the size of the test circuits increases, both methods experience an increase in wirelength and DS, making the floorplan and routing more challenging. However, under the constraint of pre-placing three fixed modules, the proposed algorithm in this paper demonstrates superior performance compared to the simulated annealing algorithm in large-scale circuit testing. Lastly, comparing the experimental results of pre-placing fixed modules in the MCNC and GSRC benchmark circuits, we can observe that the former outperforms the latter, indicating that pre-placing fixed modules makes the floorplanning problem more complex and challenging.
## 4 Experiments and Results  
In order to validate the effectiveness of RL-Fusion and its performance improvement on specific tasks, this study evaluated model fusion through ablation experiments (using reinforcement learning alone, RL-Fusion, and RL-Fusion+Finetune by fine-tuning the pre-fusion pedestal model on named entity recognition data, respectively). Experiments were conducted using the MMLU [19] and C-Eval [20] datasets to validate RL-Fusion's improvement in cross-linguistic capabilities, and the Yayi [21] dataset was used to test its performance improvement in the named entity recognition task. In addition, to compare the effectiveness of RL-Fusion with the benchmark model, we conducted comparative experiments on the above datasets. To demonstrate the advantages of RL-Fusion in enhancing the capabilities of specific tasks and its efficiency in resource utilization, we also conducted a resource consumption comparison with the traditional fine-tuning approach and a side-by-side comparative analysis with other models with the same amount of parameters on the MMLU and C-Eval datasets.
## 4 Conclusions  
This paper presents a hybrid prediction model, MHA-ConvLSTM, for dam deformation that takes into account the lag between environmental factors and deformation. The model builds upon the traditional LSTM for time series prediction by incorporating a multi-headed attention mechanism and convolution technique. Through simulation comparison tests using an active dam as an example, we demonstrated the feasibility of incorporating the lag factor and validated the generalization and effectiveness of the MHA-ConvLSTM model. The main conclusions are as follows:  
(1) A hysteresis quantification algorithm is proposed to introduce a new hysteresis factor based on the influence factor of the traditional HST model, aiming to simulate the hysteresis effect of water level on deformation. The experimental results demonstrate that the model with the introduction of the hysteresis factor shows a significant improvement in the prediction accuracy compared with the traditional model;  
(2) Compared with the plain LSTM, the MHA-ConvLSTM model is more sensitive to the hidden features of long-time domain deformation data, more sensitive to the temporal features, and more robust compared with other models. This is reflected in the fact that the predicted values fit the measured values more closely and maintain stable performance in the prediction of multiple sets of experimental data, and the evaluation indexes such as RMSE can be improved by more than 50% compared with the traditional model;  
(3) The experimental findings demonstrate the applicability of the MHA-ConvLSTM model put forward in this paper for real-world engineering. The hysteresis effect of water level and temperature on deformation can simultaneously be considered in later study, as can hyperparameter optimization.  
Not applicable.  
The authors declare no conflict of interest.
# Improved Mask R-CNN Combined with Otsu Preprocessing for Rice Panicle Detection and Segmentation
###### Abstract  
Rice yield is closely related to the number and proportional area of rice panicles. Currently, rice panicle information is acquired with manual observation, which is inefficient and subjective. To solve this problem, we propose an improved Mask R-CNN combined with Otsu preprocessing for rice detection and segmentation. This method first constructs a rice dataset for rice images in a large field environment, expands the dataset using data augmentation, and then uses LabelMe to label the rice panicles. The optimized Mask R-CNN is used as a rice detection and segmentation model. Actual rice panicle images are preprocessed by the Otsu algorithm and input into the model, which yields accurate rice panicle detection and segmentation results using the structural similarity and perceptual hash value as the measurement criteria. The results show that the proposed method has the highest detection and segmentation accuracy for rice panicles among the compared algorithms. When further calculating the number and relative proportional area of the rice panicles, the average error of the number of rice panicles is 16.73% with a minimum error of 5.39%, and the error of the relative proportional of rice panicles does not exceed 5%, with a minimum error of 1.97% and an average error of 3.90%. The improved Mask R-CNN combined with Otsu preprocessing for rice panicle detection and segmentation proposed in this paper can operate well in a large field environment, making it highly suitable for rice growth monitoring and yield estimation.  
rice panicles; instance segmentation; Mask R-CNN; image processing; model optimization 2022
# Scene Equipment Saving and Loading Method for Digital Twin Workshop
## 2 Related Work
Objects such as tools, machines, products and operators in the workshop were described using attribute and relational information; the data were transferred according to the MTConnect standard. [PERSON] et al. [30] introduced the CPS system architecture for DT establishment. It divided the DT architecture into four layers, i.e., the physical layer, the data extraction and integration layer, the cyberspace layer and the interaction layer. In the data extraction and integration layer, data packets from the physical system can be received, normalized into machine-readable form and finally passed to the network layer. [PERSON] et al. [31] proposed a DT-driven machining quality traceability and dynamic control method. A Bayesian network model was used to analyze the influence weights of factors affecting machining quality, and this was used to build an information model for machining quality traceability. In the above study, a broader range of workshop objects were modeled and analyzed, such as a machining unit [26; 31], workshop assembly lines [27] and workshop production lines [28; 29; 30]. However, at this stage, the digital simulation of workshop objects has a non-retrogressive character in the time dimension and there is currently no way to save and load the action states of workshop objects. This makes it challenging to analyze workshop manufacturing bottlenecks through historical data and equipment historical states. The information from the cited works is presented in Table 1. \begin{table}
\begin{tabular}{l l l} \hline
**Reference** & **Modeling Objects** & **Support Time Backtracking?** \\ \hline
[24] & Cutting Tool & Negative \\
[25] & Machine tool & Negative \\
[26] & Machining unit & Negative \\
[27] & Workshop assembly lines & Negative \\
[28] & Workshop production lines & Negative \\
[29] & Workshop production lines & Negative \\
[30] & Workshop production lines & Negative \\
[31] & Machining unit & Negative \\ \hline \end{tabular}
\end{table} 
Table 1: Information from cited works. The above analysis of the literature reveals that there are still some research gaps at this stage:  
1. Some studies do not model workshop information comprehensively enough, focusing only on a single piece of equipment or a component of the equipment, resulting in multiple sources of workshop information not being fully utilized. 2. The workshop software at the present stage still has the one-way characteristics of time passing and non-returnability and lacks an effective means to return the workshop history status. 3. Previous studies have lacked an effective means of building a modular system for saving and loading data from multiple sources in the workshop. The above points are important, as they clarify the logical relationship of the multi-source heterogeneous information on the workshop and reproduce the historical action state of the workshop, helping the workshop administrator find the implicit bottlenecks. This is the focus of this study and will be discussed further in the following sections.
## 3 Results and Discussion  
### Distribution of Temperature Field in Fractured Granite  
To gain an intuitive understanding of the heat transfer process in fractured granite, cloud diagrams illustrating the temperature field distribution in fractured granite were drawn. The temperature field distribution of the fractured granite at various times under a heat source temperature of 80 \({}^{\circ}\)C is shown in Figure 4.  
Figure 4: _Cont_.  
Figure 3: The position of embedded temperature sensors and monitoring lines in the fractured granite sample.  
It can be seen from Figure 4 that during the initial 10 min, although the isolines are horizontally distributed overall, there are still temperature fluctuations and cusps at the same distance from the heat source (\(d_{\rm h}\)). This phenomenon can be attributed to the fact that despite the granite being structurally uniform as a whole, in reality, its interior is a polycrystalline composite structure formed by quartz, feldspar, mica, and other crystals, so there is a significant inhomogeneity within the granite, which eventually causes local temperature fluctuations. At 35 min, the shape of the temperature contour undergoes a significant change, which is mainly attributed to the interference of the fracture. Due to the existence of the fracture, the temperature distribution cloud map gradually shows that the temperature on the lower side of the fracture is higher than that on the upper side. After 60 min, the shape of the temperature contour map tends to stabilize, and only the numerical value shows a gradual increase. In summary, the inherent heterogeneity of granite and the fracture's presence significantly influence the temperature field's shape and spatial distribution.  
Regarding the impact of the fracture on the distribution of the temperature field in granite, the fracture does not yet affect the temperature field distribution at 5 min. At 10 min, the lower-left corner of the fracture begins to affect heat transfer within the granite slightly. At 35 min, the impact of the fracture on heat transfer in granite is significantly enhanced, which is specifically reflected in the heat concentration in the region of 40-180 mm in the x-direction. This is due to the low thermal conductivity of air in the fracture, which impedes the rapid upward transfer of heat along the vertical direction and eventually leads to the significant difference in the distribution of the internal temperature field of the granite  
Figure 4: Temperature field distribution of fractured granite at different times under the heat source temperature of 80 \({}^{\circ}\)C: (**a**) 5 min; (**b**) 10 min; (**c**) 35 min; (**d**) 60 min; (**e**) 300 min; (**f**) 540 min.  
between the two sides of the fracture. At 60 min, the heat accumulation phenomenon in the 40 to 120 mm area in the x-direction is further intensified. After 60 min, the heat concentration at the lower side of the fracture in this area continues to exist.
## 4 Net Energy Production in MCs  
Maximizing the biomethane recovery from anaerobic effluents and thus harvesting the useful energy is the major goal of utilizing membrane contactors. As mentioned in the previous sections, the biomethane recovery efficiency heavily depends on both membrane inherent parameters and operating parameters. A number of studies have reported the methane recovery efficiencies of hollow fiber MCs to demonstrate the potential benefits of the processes. More importantly, economic analysis in terms of net energy production has also been conducted in some studies by considering the cost and energy required for the operation of MCs. For example, [PERSON] et al. conducted economic analysis based on the modelling data of a MC comprising hydrophobic hollow fiber membranes [42]. They predicted positive net energy production values (E\({}_{\text{NET}}\)), which can be further increased by optimizing the operation conditions. Later, [PERSON] et al. analyzed the percent methane recoveries from AnMBR and UASB effluents for a MC employing a surface-modified PDMS-PVDF membrane [40]. The membrane performance was maintained for 7 days with a mild fouling propensity when the liquid velocity was fixed at 0.06 m s\({}^{-1}\) under vacuum operation. In addition to the methane recovery efficiency, they also carried out the economic analysis and provide E\({}_{\text{NET}}\) values as summarized in Table 3. Although the methane recovery performance of the PDMS-only membrane was found to be the greatest among the four candidates, the recovery efficiency of the PDMS-only membrane  
Figure 6: (**a**) Schematic illustrations of the solvent-based membrane contactor (SMC) process for methane extraction and (**b**) time traces of normalized methane mass in the feed and draw solutions at 25 \({}^{\circ}\)C for SMC experiments. Reproduced with permission from ref [83]. Copyright 2020 Elsevier.  
was significantly degraded due to the membrane fouling after 3 days. Eventually, the membrane with anti-fouling layer (PDMS-co-P1) could extract 3% more energy than the PDMS-only membrane, implying that the control of membrane fouling is highly important to secure the net energy production. It is noteworthy that such difference in net energy production would become greater and more evident during the long-term operations.
# Power Modeling and Experiment Study of Large Flapping-Wing Flying Robot during Forward Flight
## 2 Materials and Methods
### Power Model of the Flapping-Wing Air Vehicle in Forward Flight  
Flapping-wing air vehicles, similar to birds, need energy to maintain flight. A FWAV uses its energy to generate lift and overcome drag in flight. The power consumption of the FWAV during horizontal forward flight consists mostly of two components: aerodynamic power and inertial power. The resistance of the air movement used causes the aerodynamic drag that acts on the flapping-wing vehicle. The power required for wing motion during flight is known as inertial power, and it is very low in the event of tiny wing-flapping amplitude and low flapping frequency. During the flight of the HIT-Hawk designed in this study, however, the inertial power is not negligible.
## 5 Conclusions  
This paper introduces the additive manufacturing of an Invar mold using three-dimensional printing with arc-DED technology. However, its success is not solely based on this; it relies on the development of online inspection and monitoring techniques. Some of the conclusions that can be drawn are as follows:  
* A range of manufacturing conditions has been studied, considering the feed rate and wire speed in the deposition of Invar.
* The geometry of the weld bead has been systematically characterized for various welding parameters. It has been observed that profiles obtained through the direct measurement of the bead using a laser profilometer consistently align with a parabolic regression. The quantitative assessment reveals that R-squared values for the regression, derived from the 46 profiles taken from the initial tested bead, range from 0.807 to 0.948, with a mean value of 0.887. This statistical analysis indicates that, on average, the model explains approximately 88.7% of the variability in the data, affirming the robustness of the parabolic regression fit to the observed bead profiles.
* Monitoring data have been extracted throughout the fabrication process of an Invar mold using arc-DED technology. The data collection spanned a duration of 1 h and 34 min, employing a strategy that adapted the conditions.
* A virtual sensor methodology has been established, enabling the indirect measurement of bead geometry through a neural network model, fed with process monitoring data. This innovative approach employs a feedforward neural network (FNN), where the internal signals act as input, and the resulting height and width of the bead constitute the output. Moreover, this neural network model has demonstrated a robust performance, with a test loss of 0.144 and a test accuracy reaching 1.0, underscoring its efficacy in predicting weld bead geometry based on process parameters.
* Measurements made with the profilometer, and the virtual sensor have been compared, showing a high level of precision.  
Future lines of research aim to extend the model to beads above the zero bead, as well as to explore other materials and additive manufacturing technologies.  
Conceptualization, F.V. and A.S.; data curation, A.F.-Z. and F.V.; formal analysis, A.F.-Z. and F.V.; investigation, F.V. and A.S.; methodology, A.F.-Z. and F.V.; project administration, A.S. and J.R.A.L.; supervision, J.R.A.L. and A.S.; validation, J.R.A.L. and A.S.; writing--original draft, A.F.-Z. and F.V.; writing--review and editing, A.F.-Z., J.R.A.L. and A.S. All authors have read and agreed to the published version of the manuscript.  
This study was supported as part of the ADDILANZA project by the Euroregion Nouvelle-Aquitaine Euskadi Navarra under the \"Euroregional Innovation\" program.  
Not applicable.  
Data Availability Statement: The data presented in this study are available on request from the corresponding author.
# A Novel Deep Learning Method for Recognition and Classification of Brain Tumors from MRI Images
## 3 Proposed Methodology
### Tumor Localization and Segmentation Using Mask-RCNN
#### 3.3.1 Feature Extraction  
The backbone network is employed to obtain the relevant feature from the input MR images [59]. This network could be any CNN model intended for image analysis, such as ResNet-50, ResNet-101, and DenseNet. A keypoints extraction network must be intensely adequate with many convolution layers such that it can appropriately learn reliable and more discriminating features. However, the increase in network depth size adds more computational overhead and makes it difficult to optimize the network weights, which may result in an exploding gradients problem. The work in history has employed ResNet for medical image analysis with Mask-RCNN. However, the ResNet model uses skip-connections and comprises many parameters, which eventually results in the vanishing gradient problem. In the presented work, we have implemented Mask-RCNN with both the ResNet and DenseNet framework. More specifically, we have computed features with ResNet-50 and DenseNet-41 frameworks. Since DenseNet contains dense connections, this results in computing a more representative set of image features.  
The DenseNet-41 has two potential differences from the traditional DenseNet: (i) DenseNet-41 has fewer parameters from the actual model as instead of 64, it has 24 channels on the first convolution layer and the size of the kernel is 3 \(\times\) 3 instead of 7 \(\times\) 7; (ii) the number of layers within each dense block is attuned to deal with the computational complexity. The dense block is the fundamental part of DenseNet-41, as shown in Figure 4.  
Figure 3: The structure of the proposed technique.
## 4 Applications
### Smart Agriculture  
In modern agriculture, crop health monitoring and the application of pesticides and fertilizers are pivotal for increasing yields and reducing environmental pollution. Supported by SAGINs, drones equipped with sensors and imaging devices can capture high-resolution images of farmland. Advanced data analysis methods are employed to assess soil nutrient composition and moisture levels, enabling the real-time monitoring of soil conditions, crop growth, and climate changes. These data underpin precision fertilization and pesticide application, while coordinating with ground control centers and satellites to ensure real-time information sharing and accurate task execution [157]. Such an approach minimizes the environmental impact of fertilization and pesticide application, while enhancing crop productivity and quality.