esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Evaluation of the Antioxidant Activity of Extracts and Edible Coating Solutions
#### 2.5.2 Radical 2,2-Diffiphenyl-1-Pylrilhydrazine (DPPH) Scavenging Activity  
The antioxidant activity of _P. canaliculata_ extracts and coating solutions was assessed using the 2,2-diphenyl-1-picrylhydrazyl (DPPH) radical scavenging assay, as described by [PERSON] et al. (2021) [32]. Briefly, 10 \(\upmu\)L aliquots of either the coating solution, extract  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Coating Solution** & **Polysaccharide (\% \(m/v\))** & **Glycerol (\% \(m/v\))** & **Tween 80 (\% \(m/v\))** & **P. canaliculata EtOH** \\  & & & & **Extract (\% \(m/v\))** \\ \hline Carrageenan (Car) & 1 & 0.75 & 0.02 & 0 \\ Carrageenan with _P._ & & & \\ _ canaliculata_ extract & 1 & 0.75 & 0.02 & 1 \\ (Car Pc) & & & & \\ Alginate (Alg) & 2 & 0.6 & 0 & 0 \\ Alginate with _P._ & & & \\ _ canaliculata_ extract & 2 & 0.6 & 0 & 0.5 \\ (Alg Pc) & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Formulation of edible coating solutions tested in mackerel fillets.  
solution (1 mg/mL), distilled water (blank) or Trolox standard solutions (10 to 750 ug/mL), were added to 990 \(\mu\)L of an ethanolic DPPH solution (40 ug/mL). The mixtures were homogenized and incubated at room temperature in the dark for 30 min. Lastly, 200 \(\mu\)L of each reaction mixture was transferred, in quadruplicate, to a 96-well plate, and the absorbance was measured at 517 nm using a microplate reader. Antioxidant activity was expressed as Trolox equivalents (Hg/mg of extract or \(\mu\)g/mL of coating solution). For coating solutions supplemented with _P. canaliculata_ ethanolic extract, the concentration of extract required to inhibit 50% of DPPH radical activity (IC\({}_{50}\)) was also determined and expressed as % (\(m/v\)).
# A Progressive Semantic-Aware Fusion Network for Remote Sensing Object Detection
## 4 Experimental Results and Discussion
### Experimental Details  
Our experiments operate within a Windows 11 environment, constructed using PyTorch 2.0.1. Algorithmic implementations leverage an NVIDIA RTX 4070 Ti GPU (12 GB VRAM) for accelerated processing. Optimization protocols adopt stochastic gradient descent (SGD) with an initialized learning rate of 0.01, complemented by \(L_{2}\) regularization (\(\lambda\) = 0.0005) and momentum coefficient of 0.937. Training procedures utilize mini-batches of 16 samples, with input imagery standardized to 640 \(\times\) 640 resolution across all benchmark datasets. Model evaluation employs the mean average precision ([EMAIL_ADDRESS]) metric, calculated at the intersection-over-union threshold of 0.5 for detection quality assessment.
## 2 The Formulation of the Multi-Material Topology Optimization Problem
### Filtering and Projection  
In order to resolve mesh-dependent and checker-board issues, a density filter is used here [19], which is already provided in the 88-line code.  
\[\tilde{x}_{i}^{e}=\frac{\sum_{j\in N_{e}}H_{ej}x_{i}^{j}}{\sum_{j\in N_{e}}H_ {ej}} \tag{2}\]  
where \(N_{e}\) is the neighborhood of an element \(x_{i}^{e}\), which is defined as \(N_{e}=\{j:dist(e,j)\leq R\}\). \(R\) is the filter size. \(H_{ej}\) is a weight factor, defined as \(H_{ej}=max(0,R-dist(e,j))\). \(\tilde{x}\) is the filtered density.  
To avoid a gray element for the optimized structures and to ensure a 0-1 solution, the _tanh_ function-based projection function is used, which can be written as [33; 34]:  
\[\overline{\tilde{x}_{i}}=\frac{tanh(\beta\eta)+tanh\big{(}\beta\big{(}\tilde{ x}_{i}^{e}-\eta\big{)}\big{)}}{tanh(\beta\eta)+tanh(\beta(1-\eta))} \tag{3}\]  
where parameter \(\beta\) is used to control the slope of the function; parameter \(\eta\) defines the center of the smooth transition part of the function and is selected as 0.5 in this paper.
## 3 Materials and Methods
### Evaluation
#### 3.4.2 Classification Threshold Adjustment  
Depending on the application, binary classification often needs to optimize specific metrics, such as the minimization of FNR or FPR (the same as maximizing TNR or TPR). Sometimes, the default threshold (0.5) may not represent an optimal interpretation of the predicted probabilities. It is the case when the predicted probabilities are not calibrated, which is a known problem for modern neural networks [58]. Also, when the cost of one type of misclassification is more important, changing the default decision threshold is one way to handle it. According to [59], misclassifying an actual positive example into a negative is often more expensive than an actual negative example into a positive. Thresholding is a cost-sensitive meta-learning method described in [60], based on the selection of the probability that minimizes the total misclassification cost on the training instances as the threshold for predicting testing instances. Therefore, when interpreting the predictions of a model, sometimes there is a need to change the default decision threshold of 0.5. If TPR and TNR have the same importance for the L1 trigger, one way of calculating the cut-off is using [PERSON]'s J statistic ([61]), defined as follows:  
\[J=Sensitivity+Specificity-1, \tag{10}\]  
or simplified:  
\[J=TPR-FPR. \tag{11}\]  
[PERSON]'s index is often used in combination with ROC curve analysis. The index is calculated for all points of an ROC curve, and the optimal threshold with the largest J value is chosen. If both metrics, FNR and FPR, are not equally important, using the trial and error method helps find the threshold to lower the targeted metric.
## 4 Results and Discussion
### Full-Scale Model Results
#### 4.1.4 Lateral Displacement of the Wall Facing  
The experimental results indicated that the lateral displacement of the wall at the top of panel 2 (z = 1.5 m) was smaller than that at the top of panel 3 (z = 0.75 m), and the largest lateral displacement was observed at the top of panel 4 (top of the wall; z = 0 m) (as shown in Figure 12). At the failure load level (302 kN/m\({}^{2}\)), the maximum lateral displacement at the top of the wall was 3899 \(\upmu\)m, which was much smaller than the allowable lateral displacement for the wall (\(\Delta\) = H/100 = 3 cm) [8; 10]. The lateral displacement of the wall in this study had a similar pattern to the displacement profiles in previous studies [36].
## 4 Discussion
### Limitations of the Research and Future Perspectives  
Until 2022, judokas with visual impairment were classified into three levels based on their degree of visual impairment (B1: blind; B2: severely impaired vision; and B3: moderate to poor vision). Recently, the IBSA modified the visual classification rules for judo as well as the competition rules [40]. As a result, the visual categories (B1, B2, and B3) were reduced from three to two (J1 and J2): J1 for blind judokas and J2 for partially sighted judokas. In addition to modifying the visual categories, the competition format also changed. Now, judokas fight against opponents from the same visual category (J1 vs. J1 and J2 vs. J2). Previously, all athletes competed together, regardless of their visual classification. These modifications also led to an adjustment in weight categories [40]. Men's weight categories were reduced from seven (\(-60\) kg, \(-66\) kg, \(-73\) kg, \(-81\) kg, \(-90\) kg, \(-100\) kg, and \(+100\) kg) to four (\(-60\) kg, \(-73\) kg, \(-90\) kg, and \(+90\) kg).  
Due to these changes, we consider that the reduction in weight categories may have altered the pattern of scoring actions. Therefore, the results obtained in this study should be interpreted with caution. Additionally, this situation opens up new opportunities for future research.  
Another future line of research would be to replicate this study in women, considering the new weight categories. The women's weight categories were reduced from six (\(-48\) kg, \(-52\) kg, \(-57\) kg, \(-63\) kg, \(-70\) kg, and \(+70\) kg) to four (\(-48\) kg, \(-57\) kg, \(-70\) kg, and \(+70\) kg).  
Regarding judo for visually impaired athletes, it is crucial to highlight the impact of visual category on competitive performance. It has been found that para-judokas in category B1 (with total blindness) tend to perform worse compared to para-judokas in categories B2 or B3, who have partial residual vision [13,14]. These differences suggest that visual ability, even if limited, can be an important factor influencing the effectiveness of technical actions and scoring ability. Therefore, given the recent rule changes, we consider it highly relevant to conduct this study by stratifying data not only by weight category but also by visual category, to determine the scoring action pattern in J1 and J2 judokas.  
Finally, another limitation of this study is that we did not analyze how judokas evade attacks, which often trigger counterattacks and re-counterattacks that frequently result in wazza-ari. Future research that incorporates these counterattack patterns could provide a more detailed understanding of scoring dynamics in judo for visually impaired judokas.
## 6 Simulation Results
### Tension Springs Problem  
In this section, the effectiveness of the HABC in designing tension springs is evaluated. The schematic view of this problem is depicted in Figure 8 and can be expressed using Equations (21)-(26) [22].  
\[f(X)=(x_{3}+2)x_{2}x_{1}^{2} \tag{21}\]  
Subjected to  
\[g_{1}(X)=1-\frac{x_{3}x_{2}^{3}}{71,785x_{1}^{4}}\leq 0 \tag{22}\]  
\[g_{2}(X)=\frac{4x_{2}^{2}-x_{1}x_{2}}{12,566\big{(}x_{2}x_{1}^{3}-x_{1}^{4} \big{)}}+\frac{1}{5108x_{1}^{2}}-1\leq 0 \tag{23}\]  
\[g_{3}(X)=1-\frac{140.45x_{1}}{x_{2}^{2}x_{3}}\leq 0 \tag{24}\]  
\[g_{4}(X)=\frac{x_{1}+x_{2}}{1.5}-1\leq 0 \tag{25}\]  
\[0.05\leq x_{1}\leq 2.00,\ \ \ 0.25\leq x_{2}\leq 1.30,\ \ 2\leq x_{3}\leq 15 \tag{26}\]  
where \(x_{1}\) = wire diameter (d), \(x_{2}\) = mean coil diameter (D), and \(x_{3}\) = the number of active coils (N).  
The outcomes of the algorithms applied to the tension spring problem are presented in Table 3. As demonstrated, HABC obtained the optimal value of the objective function, which was 0.012653. The convergence curve of the algorithms for this problem is depicted in Figure 9, where it can be observed that HABC converges more rapidly than other algorithms.  
Figure 8: Schematic view of the tension spring problem.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Algorithm** & **Best F(x)** & **Mean F(x)** & **SD F(x)** \\ \hline HABC & \(1.26653\times\mathbf{10^{-2}}\) & \(1.28845\times\mathbf{10^{-2}}\) & 0.0000274 \\ \hline OA & \(1.26678\times\mathbf{10^{-2}}\) & \(1.29508\times\mathbf{10^{-2}}\) & 0.0001456 \\ \hline GWO & \(1.27253\times\mathbf{10^{-2}}\) & \(1.38786\times\mathbf{10^{-2}}\) & 0.0189652 \\ \hline ABC & \(1.27662\times\mathbf{10^{-2}}\) & \(1.41256\times\mathbf{10^{-2}}\) & 1.2451960 \\ \hline BBO & \(1.27723\times\mathbf{10^{-2}}\) & \(1.55412\times\mathbf{10^{-2}}\) & 2.9125355 \\ \hline PSO & \(1.27945\times\mathbf{10^{-2}}\) & \(1.96352\times\mathbf{10^{-2}}\) & 4.0189632 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The outcomes of the proposed algorithms for the tension spring problem.
## 4 Performance Evaluation and Discussion
### Discussion  
This model can automatically process and analyse a large number of different types of payment data, reducing the effort and time required for manual processing; it can handle variable-length sequence inputs and is suitable for processing payment information text of varying lengths. Furthermore, because the LSTM model models the sequence and time dependence of the input sequence, it is robust to noise and variation in the input data, enabling faster and more accurate detection of payment problem areas.  
However, the performance of the model is highly dependent on the quality of the data and the accuracy of the annotations. The accuracy of the model can be affected if there is noise or incorrect annotations in the training dataset, and the distribution of samples in the payment information detection task can be unbalanced, leading to a lower prediction accuracy for models with fewer samples and require certain strategies to deal with the data imbalance.  
Privacy and security are very important considerations when processing payment information. Appropriate security measures must be taken to protect users' payment information to avoid potential data leakage or misuse. Future research should continue to explore how to design more secure and reliable models to prevent data leakage and misuse, introduce more sophisticated LSTM variants or use other model structures to better capture the semantics and context of payment information, and further explore and develop data expansion techniques for payment information detection tasks to extend the training data and improve the robustness of the models.
# SA-SGRU: Combining Improved Self-Attention and Skip-GRU for Text Classification
## 4 Experiment and Results
### Results Analysis
#### 4.3.3 The Effect of Sequence Length on Model  
When dealing with text sequences in text classification tasks, their length is usually fixed. However, since each sentence is of a different length and contains different information, it is important to choose an appropriate text sequence length. In this part, we will explore the impact of different text sequences on the classification of the model. The average length of data in AGNew is 45, the average length of data in IMDB is 223.27, and the average length of data in R8 is 65.72. The sentences in each dataset are set up as five groups of sequences of different lengths. If the text length is less than the predetermined processing length, the zero mark will be padded, and if the text exceeds the predetermined processing length, it will be truncated. Figure 6 shows the three datasets with different text sequence lengths and the corresponding experimental results.  
The results are shown in Figure 6. It can be seen from Figure 6a,b that, based on the average length of the sequence in the two short text datasets AGNews and R8, appropriately increasing the length of the text sequence can improve the classification performance of Skip-GRU. Too long text sequences will not only not improve the classification performance, but too long sentence sequences will also cause words with less semantic connection to participate in the derivation of keywords, affect the weight of keyword assignment, and may also lead to degradation of classification performance and an increase in running time. From Figure 6c, we can see that when the sequence length does not reach the average length, the model classification effect increases with the sequence length. When the length of the text sequence exceeds the average length, the classification effect of the model is improved to a certain extent, but the improvement effect is not obvious.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **AGNews** & **IMDB** & **R8** \\ \hline CNN & 86.32\% & 78.45\% & 90.02\% \\ GRU+self-attention & 88.26\% & 85.78\% & 93.41\% \\ Multi-CNN & 90.84\% & 80.13\% & 93.70\% \\ Skip-GRU+improved self-attention & 88.65\% & 85.41\% & 93.66\% \\ \hline SA-SGRU(Proposed) & 93.18\% & 90.22\% & 94.51\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Effects of each part of SA-SGRU on classification accuracy.
## 2 Materials and Methods
### Typical CubeSat Software Structure
In this approach, the utilization of the centralized EventHub and EventBus, which is used for the data and commands exchange, can be enhanced by the introduction of the \"exposed\" EventBus to the communication modules of the other CubeSat components. In this way, the whole system control can be synchronized and orchestrated by the \"Saga\" pattern or similar. At the same time, such an approach solves the problem of complex and non-uniformed multiple firmware files over the different modules and thus reduces overall project complexity. The majority of the development work moves to the OBC/CDHM side and requires a system architecture response to make it easier to handle, i.e., by using design patterns like \"Saga\" and adopting a proper database. Surely, the modern approach of using RTOSes, aka FreeRTOS, embOS, and Salvo, allows for a proper abstraction level and the modularity of the complex OBC/CDHM to be designed properly too. However, the main problem of the monolithic firmware, which is still the industry practice, remains active. The monolithic implementation approach of such logically broken down and modularized software brings challenges that are typical for both waterfall development models and overall software quality complexity and cost. Normally, these challenges are integration complexity and time, the many iterations of the re-design of the data exchange and APIs, complex bug-finding and fixing, etc.
## 2 Materials and Methods
### Data Analysis  
All data analyses were performed by a computer software program (SPSS version 25.0, SPSS Inc., Chicago, IL, USA). Prior to the data analyses, a Kolmogorov-Smirnov test was applied to check the normality of data. All variables exhibited normal distribution. Descriptive statistics, including mean, standard deviation (SD), and 95% confidence interval were computed for all variables. The repeated analysis of variances (ANOVAs) for conditions (WGE vs. placebo) by time (baseline, 2 h, 48 h, 96 h after acute exercise) were applied to assess the effects of WGE on dependent variables. If any significant interaction or main effects were identified, paired test for condition effects and repeated ANOVA with least significant difference (LSD) post-hoc test for time effects were applied. The effect size was presented as a partial eta-squared (\(\eta_{\text{p}}\)\({}^{2}\)) value. The effect size can be considered as \"large effect\", if the value is 0.14 or higher [38]. The level of statistical significance was set at 0.05.
###### Abstract  
Earth observation payloads deployed outside the space station play a pivotal role in remote sensing and atmospheric monitoring. Calculating the visible time window (VTW) for regional targets is a fundamental aspect of payload observation and operational mission planning. Aiming to address the challenge of rapidly calculating the VTW for regional targets, this paper introduces the Triple Time Interval Hybridization Strategy (TTIHS). By integrating orbit filtering, large-step angle calculation, and a short interval binary search, the TTIHS adapts different strategies within global, coarse search, and precise search time intervals, effectively reducing computational workload and improving speed. Simultaneously, optimization analysis is conducted on the time step in the coarse search, achieving a balance between computation speed and accuracy. Simulation results, based on real data from the China Space Station, highlight a significant improvement in computational speed while ensuring accuracy. Specifically, the proposed rapid algorithm exhibits a 2.73-fold enhancement in speed compared to the existing rapid algorithm.  
space station; earth observation payload; regional targets; visible time window; rapid algorithm +
Footnote †: journal: Applied sciences  
Article
## 4 Experiments and Discussions
### Datasets Comparison Experiment
#### 4.3.3 Result Analysis
It was mainly used for defect image acquisition of complex structural parts such as automobile combination teeth (Figure 18b). In addition, the proposed method can also be applied to image acquisition of high reflective workpieces (Figure 19a) and transparent workpieces (Figure 19b). In the specific application scenario, the actual acquisition system can be set-up according to our method.
## 2 Materials and Methods
### Models
#### 2.3.1 Direct ERA5-Land Comparison  
In the first step, the observed variables were directly compared to the corresponding variables from the ERA5-Land data. The ERA5- Land data are constructed from a very complex coupled atmosphere-ocean-land global model that assimilates some ground and satellite observation data. Direct comparison (ERA dataset) with in situ ground observations shows good agreement for some variables on the global scale [54]. However, at the local scale non-zero bias between ERA data and local observations exists [21].
# Influence of Approaching Excavation on Adjacent Segments for Twin Tunnels
## 4 Numerical Analysis Results and Comparisons
### Comparison of Numerical and Monitored Results
* The circumferential strain increment at C3 firstly develops as tensile strain and subsequently stabilizes. It shows that the impact of the approaching excavation is slight on the measured segment at the tunnel base region. * The variation of axial strain increment at A2 is similar to that at A1. The fluctuation amplitude of the former is smaller than that of the latter one. These variations indicate that the impact of the approaching excavation is significant on the measured segment at the tunnel springline and shoulder regions, which are mainly subject to the tensile compression. * The axial strain increment with small value at A3 firstly develops as tensile strain; then fluctuates towards the reverse direction; finally stabilizes in the compressive strain state. Figure 11: Variations of the longitudinal deformations in the measured segment during the approaching excavation. According to the monitoring data, Figure 13 presents the variations of the circumferential and axial strain increments in the measured segment during the approaching excavation. The results obtained by numerical simulation agree well with the monitored results, but the peak values achieved from on-site monitoring are larger than those from the numerical model. The most probable reason for this difference is due to the interference of excavation equipment, construction procedures, and ambient temperature. Furthermore, numerical simulation is conducted in an ideal condition. Ignoring the influence of longitudinal or circumferential joints will inevitably exaggerate the stiffness of the segmental linings.