esa-sceva/satcom-chunk-collection huggingface dataset:
# Displacement-Dependent Damping Inerter System for Seismic Response Control
## 3 Characteristics of DDIS
### Parametric Analysis of DDIS
#### 3.2.1 Influence of Excitation Severity  
The performance evaluation of the structure equipped with the DDIS is a non-linear problem because of the signum function, and it is definitely influenced by the severity of the external excitation. In this section, the power spectrum density is considered as the Kanai-Tajimi model; whereas, the magnitude of \(S_{w}\) varies in a wide range. A single-span, single-floor, SDOF frame structure is used for the parametric analysis, which is characterized by mass \(m=20\) ton, fundamental natural period \(T=0.54\) s, and inherent damping ratio \(\zeta=0.02\). Regarding the bilinear model of the DDIS, \(\alpha\) and \(u_{dy}\) were assumed to be 0.02 and 0.001 m [44], respectively.  
Figure 5 illustrates the variation pattern of the effect of \(\gamma_{Dis}\) and \(\gamma_{Acc}\) against the change of \(S_{w}\), where \(\mu\) of DDIS increases from 0 to 0.50. The increase of \(S_{w}\) affects the structural response and the extent to which the non-linear effects of DDIS are triggered, which leads to the increase of \(\gamma_{Dis}\) and \(\gamma_{Acc}\). In terms of the special case, i.e., a stiff-supported DDE (\(\mu=0\)), the correspondingly minimum \(\gamma_{Dis}\) and \(\gamma_{Acc}\) are quantified as 0.68 and 0.85, respectively. For any specific value of \(S_{w}\), the proposed DDIS exhibits an improved vibration mitigation effect compared with the displacement-dependent damper (which is represented by the limit case of DDIS having \(\mu=0\)). Although \(\gamma_{Dis}\) and \(\gamma_{Acc}\) of DDIS vary with the excitation severity, the advantage of DDIS over the displacement-dependent damper is definitely true for different levels of excitation severity. Furthermore, once the DDIS meets the target demand of vibration control under the excitation for the upper limit of concerned severity, it will be more effective for vibration mitigation when subjected to the excitation with lower severity (\(S_{w}>0.02\)).
## 3 Dynamic Area Optimization Based on an NN Considering Intercell Relationships
### Cost Evaluation
Table 3 lists the average time required for training and prediction for each method. GA was executed using only 1 core or without parallelization, whereas the ensemble model training was executed sequentially with one model each to ensure a fair evaluation. We employ 16,000 distributions for training and 1 distribution for each prediction. It should be noted that the proposed method requires training time, which GA does not; therefore, the training time is added at the point of 0 distribution in Figure 16. From this figure, it can be observed that at first, the proposed method falls behind the GA due to the training time; however, it gradually fills the gap and overtakes GA. Specifically, the times for the 720 and 100 models are shorter from approximately 170 and 25 distribution points, respectively, than those for the GA. This is attributed to the prediction speed of the proposed methods. The prediction times for NN+CC+Ensemble with 720 and 100 models in Table 3 are approximately 14 and 100 times faster, respectively, than those for GA. Additionally, from the perspective of the training time, it is evident that the times for NN+CC+Ensemble with 720 and 100 models in Table 3 are not long. Specifically, they are 30 and 4 h, respectively. Consi  
Figure 14: Relationship between the total throughput median and the number of extracted models for the ensemble approach. can be executed in advance and the models do not have to be updated frequently, these values are within practical limits. It should be noted that the extended time required for NN+CC+Ensemble, compared with those for NN and NN+CC, is mainly due to the number of models. Specifically, in scenarios with 720 and 100 models, the prediction times necessary for NN+CC+Ensemble are approximately 780 and 110 times longer than those required for NN+CC, respectively. Although reducing the number of models in the ensemble approach may slightly degrade the throughput, it can significantly enhance the optimization speed. Figure 16: Relationship between the total time taken for optimizing each user distribution and the number of optimized distributions. Figure 15: Relationship between the total throughput median and the training data amount. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Training Time** & **Prediction Time** \\ \hline GA & - & 657.2 s \\ NN & 149 s & 0.06 s \\ NN+CC & 146 s & 0.06 s \\ NN+CC+Ensemble & & \\ (720 models) & 30 h & 46.8 s \\ NN+CC+Ensemble & & \\ (100 models) & 4 h & 6.6 s \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Time for training and prediction for each method.
## 3 Mechanism Analysis for Active Control
### Model Parameters Assignment and Model Validation
The detailed elemental size parameters are as follows, maximum element size: 0.12, minimum element size: 0.0216, maximum element growth rate: 1.5, curvature factor: 0.6, and resolution of narrow regions: 0.5. In this case, the total numbers of the finite element mesh are 103,085 domain elements, 28,035 boundary elements and 1838 edge elements. A point force with amplitude being 1N is added at (1.0, 0.64) on the ribbed plate A. The displacement on the monitoring point (0.3, 0.21) on the ribbed plate B is calculated. It is also resolved by the analytical model, as shown in Figure 2. The results obtained by these two methods agree well, which validates the accuracy of the theoretical model. The finite element model is illustrated in Figure 3. Besides, the beam/plate interface is considered as a nonslip line connection in the theoretical model. Yet, in the numerical model, the interface is a rectangular area, and the coupling effects between the rib and plate are slightly stronger than in the analytical model. Therefore, the resonant frequencies of the ribbed plate calculated by COMSOL are slightly higher than those calculated through the analytical approach (seen in Figure 2). Furthermore, the difference for the rib-stiffness control mode will be little larger than that of the plate-stiffness control mode [15]. The ribs are located at or close to the anti-nodal locations of the mode for the rib-stiffness control modes, in which case the coupling effects of the ribs will have a more prominent effect. This explains why the difference is remarkable for the first several modes of the ribbed plate (seen in Table 3). Figure 3: The finite element model of the rib stiffened double-panel structure. Figure 2: The displacement on the monitoring point of the ribbed plate B, calculated by analytical and numerical methods.
## 2 System Model Formulation
### Overall System Architecture
If the daily schedule is predetermined, the commuter can establish the operation of V2G and G2V, and location. In this paper, it is assumed that the PV-based charging station is located at the workplace. The parking periods of EVs in the workplace are decided depending on the duration between arrival time at workplace and arrival time at home. The duration in which the EV is parked in the workplace is treated as the slot for charging/discharging of the EV. Considering the discharging of EVs, the charging/discharging process is performed on EVs having more than 10 h of predetermined parking time. Moreover, each EV is assumed to have 64 kWh capacity of battery and the target SOC value of each EV is set to 80% while parked. The initial SOC value of each EV is provided depending on the normal distribution with the mean value as 15 and the standard deviation value as 5.
## 4 Experimental Results
### Stiffness, Shear Strain Degradation and Damping Properties  
The design of geotechnical structures made of GRMs subjected to cyclic shear loading conditions (e.g., earthquake or traffic loads) will necessitate the estimation of the dynamic and cyclic behavior of the mixtures including the small-strain shear stiffness (\(G_{\text{max}}\)), shear modulus (\(G\)) degradation, and damping ratio (\(D\)).  
As shown in Figure 9, the results of bender element tests indicate that \(G_{\text{max}}\) of GRMs decreased with increasing \(VRC\) and increased with confining pressure. This is essentially consistent with previous studies on sand-rubber mixtures [28]. However, for any given confining pressure value, it has been noticed that the \(G_{\text{max}}\) of GRMs is generally greater than that of sand-rubber mixtures having the same \(VRC\)[39; 41].  
On the other hand, the results of small-strain drained cyclic triaxial tests revealed that the addition of rubber aggregates into GRMs delays the \(G\) degradation on one side and increases \(D\) on the other (Figure 10). This is an important outcome demonstrating that GRMs will perform better than gravel only under cyclic loading, maintaining their initial stiffness and dynamic properties over a larger range of shear strains. Clearly, this is due to the ability of soft rubber particles to rebound and dissipate energy.  
Figure 10: Dynamic properties of GRMs evaluated by small-strain drained cyclic triaxial tests at 100 kPa confining pressure: (**a**) shear modulus degradation and (**b**) damping ratio.  
Figure 9: Variation of small-strain shear modulus (\(G_{\text{max}}\)) of G-RL with: (**a**) \(VRC\), and (**b**) confining pressure (adopted from [16]).
## 2 Material and Methods
### Trace Element Determination  
To determine the trace element content (Co, Cr, Cu, Fe, Mn, Ni, Pb and Zn) of _Brassi-aceae_ species, 0.5 g of freeze-dried sample was weighed in a porcelain crucible. Samples were incinerated in a muffle furnace at 460 \({}^{\circ}\)C for 15 h. The ash was bleached after cooling by adding 200 \(\mu\)L of hyperpure HNO\({}_{3}\) and 1 mL of deionized water, drying this on thermostatic hotplates, and maintaining it in a muffle furnace at 460 \({}^{\circ}\)C for 1 h more. Ash recovery was performed with 100 \(\mu\)L of hyperpure HNO\({}_{3}\), making up to 10 mL with deionized water. To Cd analysis, in order to avoid Cd volatilization, 0.5 g of freeze-dried sample was placed in a Teflon vessel. Then, 3 mL of of hyperpure HNO\({}_{3}\) and 2 mL of H\({}_{2}\)O\({}_{2}\) were added to each vessel and kept for 10 min at room temperature. After sealing the vessels hermetically, they were placed in a microwave oven (Multiwave GO, [PERSON], Germany) and digested following the instrumental parameters indicated by the manufacturer. Every sample was diluted up to a volume of 20 mL with ultrapure water  
Elemental analyses for Fe (\(\lambda\) = 248.3 nm; Slit width = 0.2 nm), Mn (\(\lambda\) = 279.5 nm; Slit width = 0.2 nm) and Zn (\(\lambda\) = 213.9 nm; Slit width = 0.7 nm) were performed by flame absorption atomic spectroscopy (FAAS) with a Varian Spectra AA-50B model, equipped with standard air-acetylene flame, and single- element hollow cathode lamps. Finally, electrothermal atomic absorption spectroscopy (ET-AAS) was used for the determination of Cd, Co, Cr, Cu, Ni and Pb (Agilent Technologies model 240Z AA with a graphite furnace and autosampler). This equipment was certified by an equipment qualification report (EQR; Agilent Technologies). In this latter, analytical methodology was developed following the instrumental parameters indicated by the manufacturer with slight modifications (Table 1). For Cd and Pb analysis, a chemical modifier (200 mL solution) was prepared containing a mixture of 0.1% Paladium matrix modifier 10 g/L (Merck, Spain) plus 0.06% Magnesium nitrate hexahydrate in 10 mL HNO\({}_{3}\) hyperpure solution (69%). For each measurement, 15 \(\mu\)L of sample and 5 \(\mu\)L of modifier solution were injected. The accuracy and precision of the different analytical techniques used in determining trace element concentrations were validated by recovery experiments using Certified Reference Materials (Table 2).
## 5 Conclusions  
The presented work shows that AE can monitor the melting process of ice which is accompanied by acoustic signals observed in the ultrasonic frequency range above 30 kHz.  
To conduct measurements in a controlled environment, an actively temperature-controlled box which can be heated and cooled with Peltier elements was build. With this, measurements on melting processes of different amounts of tap water were conducted. Basic signal analysis based on the time of occurrence, amplitude and length of acoustic events enables the monitoring of the melting processes. A systematic pattern of acoustic activity during the melting process with two peaks was measured. Due to these two peaks, which occur at the beginning and the end of the melting phase, the start, as well as the end of the melting, can be clearly estimated. In addition to this, the number of acoustic  
Figure 7: A sketch of a temporal histogram of the acoustic activity during a melting process (above) and the water/ice composition inside the sample holder (below). The physical composition of the ice in the sample holder might explain the three phases in the histogram (**A**–**C**). For comparison of the sketch with the measured data, measurement of the \(m_{ice}=4.0\) g sample is shown in the upper right corner.  
events in the first peak seems to be correlated with the amount of ice. These results show that AE can monitor melting processes in systems with a good acoustic coupling between the AE sensor and the melting ice. This is especially interesting when melting ice must be identified in areas which are difficult to access, as for instance a fuel tank within an aircraft during the daily maintenance. Here, information about ice in the fuel tank would improve the safety and efficiency of draining processes which remove liquid water from the tanks in the hangar.
## 1 Introduction
The results from these two tasks are then combined to collaboratively identify pests. Specifically, the DAM-Net has three branches that learn global features, attention-enhanced precise features, and the most discriminative regions. Concurrently, the ResNet-50 improves the main task by providing texture details and global contextual information. The framework enhances its discriminative accuracy by adaptively adjusting the loss weights for each task based on its uncertainty level, prioritizing the task with higher confidence. We evaluate our framework using the D0 dataset, which comprises around 4500 images across 40 pest categories, and the IP102 dataset, which contains over 75,000 images spanning 102 categories. The framework achieves enhanced discernment and increased robustness by integrating DAM-Net's precise attention to detail with ResNet-50s broad feature extraction capabilities. The major contributions of our research are as follows:  
1. We present the DAM-Net with three branches to enhance the extraction of complex and fine-grained pest features. We also incorporate a subsidiary task based on the ResNet-50, enriching coarse-grained and global contextual information, improving the generalizability and robustness of pest recognition;
2. We enhance both learning efficiency and recognition accuracy through a shared representation. Task loss weights, determined by homoscedastic uncertainty, are automatically learned from the data. This approach handles weight initialization reliably and balances task significance;
3. An enhanced pest-recognition framework based on multi-task learning achieves accuracies of 99.7% on the D0 dataset and 74.1% on the IP102 dataset. The remainder of the study is organized as follows: Section 2 reviews the related work. Section 3 introduces the exposition of our multi-task framework. Section 4 details experimental results and corresponding analysis. Section 5 summarizes this study.
## 3 Results
### Velocity  
The magnitude of velocity was analyzed for all COP data. The less stable conditions displayed a maximum mean velocity over 30 s that was greater than the stable, baseline  
Figure 5: Approximate entropy for each condition including eyes open, feet together (EOFT), eyes closed, feet together (ECFT), eyes open, dominant foot back (EODB), eyes open, dominant foot forward (EODF), eyes closed, dominant foot back (ECDB), and eyes closed, dominant foot forward (ECDF) with standard error bars shown for participant 1 in (**A**) the anterior/posterior direction; (**B**) the medial/lateral direction, displaying the presence of a significant difference between any of the stability levels with an ANOVA test \(p\)-value less than the alpha value of 0.05.  
Figure 6: Difference in approximate entropy from the baseline, most stable eyes open, feet together (EOFT) stance with standard error bars shown for each condition from participant 1 in (**A**) the anterior/posterior direction; (**B**) the medial/lateral direction. A significant difference from the baseline EOFT condition is displayed for all stability conditions aside from the eyes closed, feet together (ECFT) condition. [PERSON]’s post hoc test results are shown with \(p\)-values less than the alpha value of 0.05 indicating significant difference from EOFT.  
condition. Additionally, for both higher and lower stability conditions, the velocity in the ML direction demonstrated a maximum magnitude that was higher than the AP direction (Figure 7).  
The magnitude of velocity was observed to be greater as stability decreased, and in most participants, the ECFTan trials showed the highest velocity of all conditions (Figure 8). ANOVA testing indicated that five out of six participants displayed significant differences between conditions with a \(p\)-value of 2.7 \(\times\) 10\({}^{-6}\) in the AP direction and 4.78 \(\times\) 10\({}^{-7}\) in the ML direction. Whether having the dominant foot forward or backward resulted in more instability varied by participant.  
A two-sided [PERSON]'s post hoc test was performed for all participants and directions which resulted in a significant ANOVA test. This analysis illustrated that the ECFTan conditions showed a significant increase in velocity from the baseline EOFT conditions more often than the others. Five out of the six participants showed a significant increase in at least one of those conditions (Figure 9). Comparatively, also in all but one participant, the ECFT condition did not show a significant difference from the baseline EOFT condition.  
Figure 7: Velocity of participant 1 from (**A**) an eyes open, feet together (EOFT) trial in the anterior/posterior direction; (**B**) an eyes open, feet together (EOFT) trial in the medial/lateral direction; (**C**) an eyes closed, feet tandem, dominant foot forward (ECFTanDF) trial in the anterior/posterior direction; and (**D**) an eyes closed, feet tandem, dominant foot forward (ECFTanDF) trial in the medial/lateral direction where the maximum velocity magnitude is lower in the more stable EOFT trial than in the less stable ECFTanDF trial.
# Unraveling the Impact of Class Imbalance on Deep-Learning Models for Medical Image Classification
## 3 Results and Analysis
### Additional Results and Analysis  
In Sections 3.2 and 3.3, a comprehensive analysis of the Neuralnet, RSNNS, and Keras packages was conducted to determine the most effective model for 2, 3, and 4-layer topologies, with a specific focus on imbalanced datasets. The optimal combination of neurons in each layer was sought, varying this parameter between 50 and 300. Neuron combinations in the range of 10 to 40 were excluded from these experiments, although some results in Table 1 for the Keras and Neuralnet packages showed models with 30, 40, and even 10 neurons (see rows 6, 10, and 14 in Table 1). For this reason, additional experiments were conducted within this range to determine if superior models to those previously discussed could be obtained. These experiments were carried out using the Neuralnet, Keras, and RSNNS packages. Despite RSNNS initially not producing models with few neurons, it was included in the analysis. The results of these additional experiments are detailed in Table 9, from which the following conclusions are drawn. The analysis of the data suggests a disparity between training accuracy and test accuracy, yet it shows a strong inclination to corroborate previous findings. This is highlighted by the wide variability in training accuracy, ranging from 56% to 96%, as opposed to the narrower test accuracy range of 54% to 82%. This pattern underscores a tendency towards overfitting in certain configurations, where excessive optimization on training data undermines the model's effectiveness with new samples. The detailed examination of the overall accuracy in tests once again confirms the limitation of this global metric in accurately projecting class-specific prediction estimates. This is demonstrated by observing the accuracy per class, highlighting the challenges that models face in achieving a uniform classification across all categories. In Tables 3 and 6-9, it is observed that a high overall test accuracy does not necessarily translate into high test accuracy across all classes. Table 10 provides a contrast between the overall test accuracy and the test accuracy per class for the most outstanding models that employ keras with convolutional layers (keras_cnn). The data in Columns 1, 2, and 3 allow for the verification of information corresponding to the table, row, and column. It is reported that the overall test accuracy for these selected models ranges from 79% to 82%. However, a detailed inspection of accuracy per class (N, V, and B) reveals more pronounced variations. Class N proves to be robust across all models, which could indicate adequate representation or clearer distinction of its characteristics within the dataset. In contrast, classes V and B exhibit lower and more erratic performance, with class V achieving accuracies ranging from 51% to 59%, and class B from 58% to 70%, suggesting potential challenges related to data representativeness or the keras_cnn model's ability to capture