esa-sceva/satcom-chunk-collection huggingface dataset:
# River Surface Space-Time Image Velocimetry Based on Dual-Channel Residual Network
###### Abstract
Therefore, new MOT detection methods based on deep learning have been widely proposed to solve the problem of traditional methods with many parameters and a poor robustness. [PERSON] et al. [24] employ a convolutional neural network to perform multi-scale optimal selection on MSIs, while [PERSON] et al. [25] utilize MobileNetV2 to construct a classification model. Similarly, [PERSON] et al. [26] apply a residual network to classify STI angles directly. However, since these classification models output discrete integer values (with a resolution of 1 or 0.5 degrees), significant quantization errors arise in flow velocity calculations. [PERSON] et al. [27] introduced a ResNet50-based regression model, enabling continuous MOT output, which has been successfully implemented in a real river. However, the method exhibits limitations in feature extraction accuracy, particularly for MOT detection in complex environments. Under such conditions, the effective texture features in STIs often become obscured by noise interference, leading to a reduced detection performance. Therefore, it is necessary to design a model with stronger feature extraction capabilities to further improve the accuracy of MOT detection. Based on the characteristic that image edge features can explicitly represent textures' orientation, this study proposes a dual-channel residual network (DCResNet) model for MOT detection. The model employs a dual-channel architecture that processes both the original STI and edge-detected STI. Unlike existing classification methods [24; 25; 26], DCResNet adopts a regression-based approach to achieve continuous MOT outputs, better reflecting the natural continuity of river flow velocities. Since regression models require the learning of continuous predictions from discrete data, they demand higher-quality datasets and more robust feature extraction capabilities. Compared to the basic regression method in [27], DCResNet innovatively combines raw STI texture features with edge-enhanced representations. The adaptive threshold Sobel operator enhances edge delineation in STIs by effectively capturing boundaries that directly characterize the texture orientation. This structural emphasis enables the model to learn discriminative motion patterns, improving MOT detection's robustness. Features extracted from both channels are weighted and fused before regression, with optimal fusion coefficients determined through systematic training on an STI dataset constructed at the Panzhihua hydrological station. The experimental results demonstrate that DCResNet outperforms single-channel models using only raw or edge-processed STIs, validating the effectiveness of the joint learning strategy. Additionally, the model exhibits strong generalization to the unknown river. Comparative evaluations against current meter measurements confirm that DCResNet achieves a higher accuracy than the FFT method.
# Optimization of Vacuum Freeze-Drying Process and Quality Evaluation of _Stropharia rugosanmulata_
## 3 Results and Discussion
### Analysis of Colony Content Results with Different Technological Parameter  
In this experiment, a natural bioactive antimicrobial peptide, nisin, was used to treat _Stropharia rugosaannulata_. Nisin demonstrates strong inhibitory activity against various food spoilage organisms and pathogens, such as _Listeria monocytogenes_, _Bacillus cereus_, _Staphylococcus aureus_, and _Escherichia coli_. As a broad-spectrum antimicrobial peptide, nisin has high-efficiency bactericidal capabilities and can effectively inhibit the growth of various Gram-positive bacteria [27]. It is widely used in food preservation to improve food safety and to extend shelf life. Compared with traditional chemical preservatives, nisin is naturally non-toxic and has been proven to be an effective natural food biopreservative [28], reducing the use of other chemical preservatives and lowering costs.  
The plate count method was used to measure the colony counts of _Stropharia rugosaannulata_ under different treatment conditions. As seen in Table 7, the application of nisin shows significant advantages in the pre-treatment process of freeze-dried _Stropharia rugosaannulata_. The experimental data show that the colony count of fresh _Stropharia rugosaannulata_ is 4.23 log CFU/g, while the colony count significantly decreases after nisin treatment, indicating nisin's significant effect on inhibiting colony growth. In contrast, the changes in colony count after direct blanching and freeze-drying are not significant, further verifying the superiority of nisin in microbial control. Nisin provides an additional safety measure by inhibiting potential contaminants that could affect the product after packaging is opened in environments such as kitchens or restaurants, where microbial contamination may occur despite the low water activity achieved by freeze-drying. Cross contamination happens when bacteria are transferred from one food item to another food item, often by way of food handling mistakes including unwashed cutting boards or countertops, kitchen tools and unwashed hands, and self-contamination [29]. Research has demonstrated nisin's efficacy in controlling foodborne pathogens such as _Listeria monocytogenes_ and _Staphylococcus aureus_, which are common in food service settings. Nisin is used in canned food products in order to prevent spoilage from thermophilic microorganisms, acting as an additive in the storage of fruit and vegetable juice, such as tomato juice and mint extract [30]. Other studies have shown that nisin can be used as a safe and effective alternative to sulfur dioxide in enhancing date plum persimmon (_Diospyros lotus_ L) without adversely affecting the physicochemical quality of it [31]. The use of nisin can enhance microbiological stability by inhibiting bacterial contaminants such as _Escherichia coli_, _Pediococcus_ sp., and _Bacillus megaterium_ during the beer production and storage [32].
## 5 Data Imbalance
### Data Imbalance Handling Techniques Future Research Directions
#### 5.5.4 Attention Mechanism  
The attention mechanism can improve the performance and interpretability of the model by automatically learning which information should be focused on or ignored. When dealing with imbalanced data, attention mechanisms can help the model better focus on the features that are more critical for classification, thereby improving the model's ability to recognize minority classes. However, this does not directly solve the problem of dataimbalance; it indirectly affects the processing effect of imbalanced data by improving the adaptability and performance of the model to imbalanced data [120,128].
## 4 Results and Discussions
### Solvent Regeneration Test  
Solvent regeneration tests for four optimum conditions were explored further. The scrubbed solution included two MEA + PZ mixed amines (No. 26 and No. 28), one MEA + DIPA (No. 27) scrubbed solution, and one MEA (No. 29) scrubbed solution. The tested temperatures were set to 100, 110, and 120 \({}^{\circ}\)C, separately. The overall heat of regeneration was 3.39-8.45 GJ/t, within the range of 2 GJ/t and 12 GJ/t reported in the literature [13; 55; 56]. Figure 8a presents a plot of q versus t showing the effect of different solvents on the heat of solvent regenerations. The plots showed that the minimum energy requirement was at 110 \({}^{\circ}\)C for mixed solvents, No. 26-No. 28, while the heat of regeneration decreased with a decrease in temperature for No. 29 (MEA single solvent). Comparing No. 26 and No. 28 (both MEA + PZ), at the same factor E (3M) but for factor B, were 20% and 15%, respectively. The final loadings were 0.0866 and 0.1349 mol-CO\({}_{2}\)/mol-solvent for No. 26 and No. 28, respectively. The heat of regeneration for No. 28 was lower than that for No. 26, suggesting a lower heat of regeneration at higher loading than that at lower loading. A similar result was reported in the literature [13]. The sequence of the heat of regeneration was in the order of MEA \(>\) MEA + PZ \(>\) MEA + DIPA (Figure 6).  
The results of individual energy estimation revealed that q\({}_{\rm sen}\) were in the range 0.45-1.43 GJ/t, q\({}_{\rm sol}\) were in the range 1.04-5.51 GJ/t, and q\({}_{\rm ads}\) were in the range of 1.83-1.93 GJ/t. The fractions (\(F\)) of individual required energies were in the range of 0.102-0.232, 0.317-0.652, and 0.229-0.577 for q\({}_{\rm sen}\), q\({}_{\rm sol}\), and q\({}_{\rm ads}\), respectively. For further discussion, the distribution in individual energy was plotted and is shown in Figure 8b, wherein q\({}_{\rm sen}\) and q\({}_{\rm sen}\) both increase with an increase in \(F\), while q\({}_{\rm ads}\) causes a small change in \(F\) in the range of 0.229-0.577. When \(F\) > 0.4, the energy required was in the order of q\({}_{\rm sol}\) > q\({}_{\rm ads}\) > q\({}_{\rm sen}\). Three major choices were found to decrease q: one is selecting a lower heat of absorption solvent; second, using a higher concentration solvent to reduce the  
Figure 7: A plot of calculated value versus measured values for \(R_{A}\) and \(K_{G}a\).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Mixed Solvent** & \(\alpha\) & **a** & **b** & **c** & **R\({}^{2}\)** \\ \hline A1 & \(3.286\times 10^{-6}\) & 3.0754 & \(-1.1424\) & 1.1148 & 0.9994 \\ A2 & \(8.256\times 10^{-12}\) & 8.4837 & 0.5876 & 1.2734 & \(<\)0.5 \\ A3 & \(5.733\) & \(-1.5210\) & \(-0.1029\) & 0.1648 & \(<\)0.5 \\ A4 & \(1.576\times 10^{-20}\) & 16.8625 & 0.7071 & 1.4307 & 0.9680 \\ A5 & \(4.146\times 10^{-14}\) & 11.7191 & 0.08486 & 0.7232 & 0.9792 \\ \hline \hline \end{tabular}
\end{table}
Table 11: Regression parameters of \(K_{G}a\) for different solvents.  
heat of evaporation; third, an increase in \(\gamma\), which can increase CO\({}_{2}\) loading and hence decreasing q.
## 3 Results and Discussion  
### Glucose and Maltose Dynamics during Fermentation and Beer Maturation  
Glucose, maltose, and ethanol in fermented wort were analysed by HPLC. Glucose content was significantly different (\(p<0.05\)) in the composite wort prior to fermentation (Figure 1A,B). In the words, glucose content was the highest in R100 (18.3 mg/L) and the least in B100 (10.21 mg/L). Substituting barley with 40% (\(w/w\)), 60% (\(w/w\)), and 80% (\(w/w\)) Riceberry rice malt was directly proportional to the glucose content in the words. This is because rice has more starch content, with amylopectin being the major component (65-85%) compared to the starch amylopectin of barley (42-71.2%) [17]. The amylopectin is hydrolysed during rice malting by the enzymes limiting dextinase and alpha glucosidase, which are in abundance in malted rice [18]. POF\({}^{-}\) and POF\({}^{+}\) utilised glucose rapidly in the words of R80B20 and R100 during 36 h fermentation (Figure 1A,B), but it was faster in POF\({}^{+}\). Yeasts of _diastticus_ variants are characterised as rapid fermenters and have optimum activity at higher temperatures of 20-35 \({}^{\circ}\)C [19]. Maltose content in composite worts reduced with increasing percentages of Riceberry rice malt between 60% (\(w/w\)) and 100% (\(w/w\)). The maltose content varied significantly (\(p<0.05\)) among the composite worts produced. The wort B100 had the maximum maltose content (Figure 1C). The high maltose content in B100 after mashing was because barley malt possesses a higher amount of beta amylase, which cleaves the non-reducing ends of the polysaccharide chain to produce maltose [20]. This enzyme activity is limited in rice malt, resulting in low maltose production during mashing [8]. Hence, barley malt additions complemented maltose content in the composite wort. The dynamics of maltose during the fermentation of each composite wort by POF\({}^{-}\) and POP\({}^{+}\) is presented in Figure 1C,D. Both POF\({}^{-}\) and POP\({}^{+}\) were able to utilise maltose sufficiently within 72 h, and the peak of the maltose metabolism was within 60-72 h of fermentation. Comparing POF\({}^{-}\) and POP\({}^{+}\), the latter had a stronger affinity to metabolise maltose during active fermentation (72 h) in each treatment (Figure 1C,D) and hence is referred to as a super-attenuating yeast [19].
## 6 Experimental Design and Results
All hidden layers used ReLU activation functions, while the output layer consisted of 2 neurons with Softmax activation. For the CSE-CIC-ID 2018 dataset in binary classification, we employed two ANN models. The first model, ANN SL, had a single hidden layer with 90 neurons. The second model, ANN ML, comprised five hidden layers with 90, 180, 90, 45, and 20 neurons, respectively. ReLU activation functions were used in the hidden layers, and the Softmax activation function was applied to the output layer, which contained 2 neurons. In multi-class classification, we only utilized the ANN ML model. However, we observed that this model lacked the capability to accurately classify the data, so the performance results for multi-class classification are not reported. Tables 11 and 12 present a comparison of the binary and multi-class classification performance, respectively, highlighting the best results. The majority of the methods achieved high performance, with ANN, DT, and RF demonstrating the best overall performance. SVM, LDA, and KNN exhibited the worst results in both binary and multi-class cases. ANN (SL and ML) showed particularly good specificity, indicating their effectiveness in recognizing attacks. Computation times are reported in Table 13, calculated for 1000 observations. DT exhibited the shortest computation time, likely due to its lower complexity compared to other methods. However, all the methods showed relatively high computation times, underscoring the need for efficient algorithms in intrusion detection systems. The obtained results validate the applicability of the proposed machine learning algorithms in intrusion detection systems. It is important to note that binary classification generally outperformed multi-class classification, as binary classification achieved precise results with low false negatives and false positives. On the other hand, multi-class classification is more computationally demanding and complex, leading to less effective outcomes, as observed in this study. Figures 6 and 7 provide graphical representations of the results obtained from the classifiers in the binary and multi-class cases, respectively. The tests were conducted on an Acer AN515-54 laptop featuring an Intel(R) Core(tm) i7-9150H processor, running Windows 10 and MATLAB version R2021b.
## 3 Results and Discussion
#### 3.1.3 Summary (Material Sweep)  
For the investigated ambient temperature of 25 \({}^{\circ}\)C and moderate air movement (with a convection coefficient of 5 W/m\({}^{2}\) K), all of the systems struggle to transport the produced heat out of the system during use phase A. In particular, the heat dissipation of the packs by surface convection is too low and is most likely a bottleneck. Therefore, systems which can take up and store high amounts of thermal energy in a short time span, such as the heat-conductive polymer II (with a high heat capacity) and the latent heat storage material (in which the heat is absorbed by the phase change), perform best and lead to the lowest maximum temperatures. The thermal conductivity of the materials is of minor importance in this step for the given conditions. The performance of the cooling system during the use phase is on the one hand important for safety reasons. However, on the other hand, it also governs the maximum temperature, which has a big influence on the length of the following cool-down phase I and thus also influences the length of the use/charge cycle.  
During the cool-down phases, high heat transfer and a low thermal mass as well as a low starting temperature are beneficial. In cool-down phase I, the high thermal conductivity of heat-conductive polymer I leads to a quick cool down (with a high negative slope of the temperature curve). However, due to the lower starting temperatures, the length of the cool-down phase of heat-conductive polymer II is only slightly longer (3.5 min, with heat conductivity that is three times lower), and due to the lower starting temperature, the use of the latent heat storage material leads to a 17.5 min shorter cool-down phase.  
When it comes to charging, all the investigated solid cooling materials result in comparable charging time spans. Differences in their material properties have only a minor influence on their performance in this step. All materials are not able to absorb and transfer the produced heat to be charged without interruption, and thus, the charging time is significantly prolonged. This clearly indicates that it is necessary to carefully design the pack-cooling system, as small differences can significantly prolong or shorten the duration of charging and operating conditions such as the ambient temperature and ventilation (convection coefficient) are of high importance.  
Comparing the influence of cool down and charging on the total length of the use/charge cycle, both are of importance. Looking at phases A-C only (till fully charged), cool-down phase I makes up about half of the time. For the whole cycle reaching room temperature (26 \({}^{\circ}\)C), cool down makes up 82-86% of the total cycle length. Improving the cool down behavior has the potential to shorten the whole process greatly.
## 2 Materials and Methods  
In this study, the Cambridge Sequential Total Energy Package (CASTEP) [22] based on density function theory (DFT) is used for all of the calculations. An ultrasoft pseudopotential [23] plane wave approach is adopted to describe the electrons. The Perdew-Burke-Ernzerhof (PBE) and general gradient approximation (GGA) [24] are used for the exchange-correlation functional. The cutoff energy is 400 eV and the 2 \(\times\) 2 \(\times\) 1 \(k\) points for both (001) and (111) surfaces are used to sample the Brillouin zone according to the Monkhorst-Pack method [25]. Spin-polarization is taken into consideration [26]. The convergence criterion of the inter-atomic forces is set to be 0.03 eV/ \(\AA\), and the energy of self-consistent calculation is 1.0 \(\times\) 10\({}^{-5}\) eV/atom. The cutoff energy and \(k\) points are obtained by careful testing and are proven to be adequate for all of the calculations in this work; higher values of these parameters shows a difference within only 5% in calculation of formation energy.  
The H-terminated diamond 2 \(\times\) 1 reconstructed (001) and (111) surfaces are used in this study as they are the most common CVD-growth diamond thin films. A vacuum distance of 10 A is adopted between the periodical slabs in the \(z\) direction to avoid interactions. The numbers of C layers on the (001) and (111) surfaces are 14 and 12, respectively, which are also obtained using the test calculations. The bottom C layer and terminated H layer are constrained to simulate continuous bulk diamonds in the lower region of the surface structure. The calculation parameters and lattice parameters are listed in Table 1.  
The formation energy is calculated using the following equation [27; 28]:  
\[E_{f}(PV_{n}H_{m})=E(PV_{n}H_{m})-E(pure)-\left[U_{p}+mU_{h}-(n+1)U_{c}\right]- q[E_{F}+E_{V}+\Delta V] \tag{1}\]  
where \(n\) and \(m\) are the number of vacancies and H atoms, respectively. In this study, it only investigates the situation that they are either zero or one. \(E(PV_{n}H_{m})\) and \(E(pure)\) represent the total energy of the diamond surface containing \(PV_{n}H_{m}\) and the pure diamond surface, respectively. \(U_{p}\), \(U_{h}\), and \(U_{c}\) denote the chemical potentials of P, H, and C, respectively, deduced from their hydrides in gas phase (PH3, H2), and bulk diamonds. \(q[E_{F}+E_{V}+\Delta V]\) is the result of exchanges of electrons or holes with the carrier reservoirs [28]. The following section focuses only on the \(E_{F}\) of impurities with neutral charges, so the last term is zero.
# A Proactive Approach to Extended Vehicle Routing Problem with Drones (EVRPD)
## 4 Materials and Methods--Implementation of the EVRPD Model
### Dedicated Genetic Algorithm  
In contrast, the proposed dedicated genetic algorithm (DGA) is metaheuristic and is designed to solve real- and industrial-sized examples of the EVRPD. It is an approximate method like any other metaheuristic. The proposed genetic algorithm (Figure 2) was an alternative to the MP solver.  
What distinguishes this algorithm from classical GA is: the dedicated representation of the EVRPD (Section 4.2.1), dedicated selection of the initial population (Section 4.2.2), constraint handling procedure (Section 4.2.3) and an evaluation procedure (Section 4.2.4) which contain elements of pre-solving the problem being solved.
# Short-Term Power Load Forecasting Using an Improved Model Integrating GCN and Transformer
## 1 Introduction
Furthermore, several researchers have attempted to use feature selection methods to conduct feature selection on multiple sequences of exogenous variables in order to achieve prediction scenarios [5; 6; 7]. However, studies in recent years have shown that due to better fitting of nonlinear features in power load data, modern methods based on deep learning models have more advantages in accuracy when facing the task of power load forecasting. In the early years, RNN-series models were even the mainstream models in this task. In various studies of power load forecasting based on RNN system models, [PERSON] [8] and [PERSON] et al. [9] proposed two different methods for the power load forecasting task based on RNNs. [PERSON] proposed a hybrid model for the task of power load forecasting. This model takes LSTM as the main body and introduces integrated strategies such as Bagging and Boosting at the same time [10]. The experimental results show that this method can effectively improve the accuracy of power load forecasting. [PERSON] et al. developed a new model composed of Fitz-Hugh Nagumo(FHN), RNNs, and feedforward neural networks and applied it to power load forecasting [11]. The research showed that this model demonstrates excellent forecasting performance. In recent years, the research focus has shifted to graph neural networks (GNNs) and Transformer architectures [12; 13; 14; 15]. [PERSON] et al. attempted to integrate GNNs and Transformer for power load forecasting and achieved good results [16]. Although the abovementioned methods continuously improve the prediction accuracy, existing studies still lack systematic consideration of the multi-scale characteristics of load data. The heterogeneous features presented by exogenous variables and load sequences in different time dimensions have not been fully deconstructed, which provides an important breakthrough for subsequent methodological innovation [17; 18]. Therefore, in this paper, with the aim of addressing this gap, a multi-scale graph Transformer method is proposed for predicting short-term power load, mainly including the following research content:  
1. A prediction method for short-term power load forecasting is proposed, including the proposed multi-scale graph Transformer model. The model is composed of a multi-scale graph convolution module and a Transformer mixed together, combining the advantages of the two networks, and can effectively extract the spatio-temporal features at different scales and the time dependence between scales. 2. The model introduces MLLA. The use of this attention enables the model to maintain its computational complexity while further modeling global features and processing long time series data. 3. The performance of the method was evaluated through experiments on three datasets. Compared to the other methods, the method proposed in this paper shows better performance.