esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-Source Data Fusion-Based Grid-Level Load Forecasting
## 3 Results
### Regional Forecasting Performance Analysis
#### 3.2.2 Monthly Rolling Forecast Performance  
Figure 4 extends our analysis to the entire month of July 2024, revealing several important temporal patterns:  
1. Daily Cyclical Pattern Capture: The forecasting model successfully captures the fundamental daily load cycles with clear distinction between weekday and weekend patterns.
2. Peak Load Magnitude Error: Consistent with the single-day analysis, the model systematically underestimates peak loads throughout the month, with discrepancies reaching up to 10% during extreme demand periods (particularly evident on 8-9 July and 17-19 July).
3. Valley Forecasting Variability: The model demonstrates inconsistent performance in predicting load valleys, sometimes overestimating (1-3 July) and other times underestimating (13-15 July) the minimum load values. This variability suggests potential instability in the model's response to low-load conditions.
4. Mid-Month Adaptation Failure: An interesting phenomenon occurs around 12-15 July, where the forecast temporarily improves before deteriorating again. This suggests that while the model may be capturing some temporal shifts in load patterns, it fails to maintain this adaptation consistently throughout the forecast horizon.
5. Weekday-Weekend Differentiation: The model demonstrates better performance on weekdays than weekends, with weekend load overestimation being particularly problematic. This indicates insufficient differentiation of calendar features in the provincial-level model.  
Figure 3: Shanghai load forecast results for 1 July 2024.  
These observations highlight several challenges in provincial-level load forecasting that merit further investigation. The systematic underestimation of peak loads suggests that despite implementing a peak-weighted loss function, the model may require additional calibration or feature engineering to adequately capture extreme demand events. The inconsistent performance across different times of day and days of the week indicates potential opportunities for temporal ensemble methods or more sophisticated calendar-based feature extraction.  
The analysis of Shanghai's forecasting performance exemplifies the challenges faced by regional dispatch centers in maintaining forecast accuracy throughout varying temporal conditions and load regimes. These findings provide a foundation for evaluating the potential advantages of grid-level integrated forecasting approaches discussed in subsequent sections.
## 3 Results and Discussion  
### AE under Tension of the Specimens from 09G2S Steel and Specimens from 12 Kh18N10T Steel  
For a better understanding of AE generation regularities during the deformation of dissimilar welded joints, at the initial stage of research, specimens of the parent metals (09G2S and 12 Kh18N10T steels) were tested by tension. Figure 3 shows the AE data such as AE hits rate and the amplitudes of AE hits, recorded under static tension of 12 Kh18N10T austenitic steel specimens. The nature of AE data recorded during loading of the 12 Kh18N10T steel specimens is consistent with the tension diagram. An increase in activity begins near the stage of elastic deformation, at a stress of about 0.5\(\sigma_{y}\), where \(\sigma_{y}\) is a yield point. The maximum AE hits rate is observed at the moment of reaching the yield point of the material, and in the stage of strain hardening, the AE hits rate decreases monotonically. The amplitudes of the AE hits change stochastically and, as a rule, do not exceed 60 dB.  
The AE data recorded during the tension of specimens made of 09G2S pearlitic steel are more complex, presumably explained by a more complex deformation mechanism associated with the presence of a yield plateau (Figure 4). At the beginning of loading with elastic deformation of the sample, an insignificant AE hits rate is observed, which is probably associated with relaxation of residual stresses. Then, the AE hits rate increases stepwise in two stages: the first burst of AE hits rate corresponds to the beginning of the yield area of 09G2S steel; the second, a more pronounced one, is observed in the yield strength region. Each of the deformation stages corresponds to the particular values of AE hits amplitudes: in the section of elastic deformation, the AE hits amplitudes do not exceed 60 dB; within the yield plateau, the characteristic values of the amplitudes increase to 65 dB, the maximum amplitudes of the AE hits (over 70 dB) are observed in the region of the yield strength of 09G2S steel.  
Figure 3: AE data recorded under tension of specimens made of 12 Kh18N10T austenitic steel.
# Path Loss Determination Using Linear and Cubic Regression Inside a Classic Tomato Greenhouse
## 2 Materials and Methods
### Hardware  
1. Raspberry Pi. The Raspberry Pi 3 computer receives data from the sensor node through the sink node connected to its USB port. The electric energy, available inside the greenhouse, fed the Raspberry Pi uninterruptedly during the testing stage.
2. Sensor and sink nodes. We used the Re-Mote nodes [74] that operate with the 2.4 GHz band (CC2538 System-on-Chip) for both the sensor node and the sink. The sink node is powered by the power provided by the USB cable connected to the Raspberry Pi computer (See Figure 2A), while the sensor node has a rechargeable Lithium-ion battery of 3.7 V and 6600 mAh (See Figure 2B).
## 3 Results and Discussion
### Comparison of Whether the New System with WHR Reduces the Cooling Load in Summer  
Figure 18 shows the reduction in the cooling load of the building during the year with and without WHR. Without WHR, the cooling load reduction of the entire building was 55,417.33 kWh. With WHR, the cooling load reduction was 28,537.57 kWh. The difference between the two was 26,879.76 kWh.
## 4 Conclusions  
In this study, electrospinning and freeze-drying techniques were combined to produce 3D nanofibrous scaffolds with porous structures, and the fabricated scaffolds were further crosslinked by EDC. Morphology study illustrated the scaffold owned interconnected porous structures with the presenting of nanofibers, and the porosities of the scaffolds were found independent to the adding of nanofibers. The mechanical strengths of the porous scaffolds can be tuned by nanofibers or chitosan/RHC concentration. Specifically, increasing the number of nanofibers led to an increase in the mechanical strength of the scaffolds. The cell viability test determined that the fabricated 3D scaffolds are non-toxic to the selected cells. Meanwhile, cell distribution proved that nanofibrous porous structures facilitated the spreading and migration of the proliferated cells as cells infiltrated into the  
Figure 7: Expression of: (**A**) collagen I, (**B**) collagen III, (**C**) MMP-13, and (**D**) vimentin in nanofibrous membranes, 3D nanofibrous scaffolds and culture dishes as measured by RT-PCR. Bars represent mean \(\pm\) SD, number of repeats \(n=3\).  
inner part of the scaffolds from day 3 to 7. Results from RT-PCR further confirmed that nanofibrous scaffolds could support the cell attachment, proliferation, and migration as the expressions of ECM-associated proteins were unregulated by the 3D scaffolds. Thus, the fabricated 3D nanofibrous scaffold shows it potentials in tissue engineering application and tissue regeneration.  
Conceptualization, A.D. and Y.Y.; methodology, A.D. and S.D.; formal analysis, S.D.; investigation, A.D. and Y.Y.; writing--original draft preparation, A.D.; writing--review and editing, Y.Y.; visualization, Y.Y.; supervision, Y.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National High Technology Research and Development Program of China (No. 2014 AA022107).  
Not applicable (This study does not involve human or animal).  
Not applicable.  
This study did not report any data.  
This work was financially supported by the National High Technology Research and Development Program of China (No. 2014 AA022107), and the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD).  
There is no conflict of interest.
## 2 Materials and Methods
### Functional Connectivity Analysis
#### 2.3.1 Global Connectivity  
Global connectivity was computed by averaging all 253 PLV values that were obtained per frequency band. This led to a total of five PLV\({}_{\text{mean}}\) values per subject (i.e., one PLV\({}_{\text{mean}}\) per frequency band). Following the Shapiro-Wilk test, a Mann-Whitney U-test was used to compare the mean PLVs between the AD and HC groups. The aim of this evaluation was to determine whether band-specific differences could be found in the global functional connectivity of the AD subjects against the HCs.
## 4 Experiments
### Comparison of Algorithm Performance  
To verify the performance of the proposed algorithm, four datasets of varying scales (438, 2098, 6370, and 20,014 images) were randomly selected from a total of 100,299 images. The proposed algorithm was applied to these datasets to comprehensively evaluate their statistical characteristics, generalization ability, and stability. Metrics such as precision, accuracy, recall, and speed were defined and compared with those of the original algorithm.
## 4 Practical Application and Result Analysis
### Optimization of Algorithm Parameters  
In this research, it is assumed that Well A1 in the studied interval has the complete RHOB curve, and the RHOB curve of Well A1 is used as the training set to establish the ELM, KELM, HKELM, and BO-HKELM models. The \"missing\" RHOB curve of Well A2 in the corresponding interval is predicted.  
The Bayesian optimization algorithm introduced in Section 2.3 is used to optimize five parameters in the BO-HKELM model, such as the polynomial kernel function parameters \(C_{0}\) and \(d\), the Gaussian radial basis kernel function width \(\sigma\), the kernel weighting coefficient \(C_{1}\), and the the regular term coefficient \(C\). These five tuning parameters control the developed HKELM's capacity for prediction, learning, and generalization. To acquire the optimized hyperparameters of the HKELM model, all of the values for each parameter \((C_{0},C_{1},d,\sigma,C)\) are placed in five distinct vectors. The goal of the optimization procedure is to compare the different parameter combinations and select the optimal \((C_{0},C_{1},d,\sigma,C)\) values with the lowest cost function. The fitness (RMSE) reduction rate in the training phase of HKELM in Well A1 is shown in Figure 6.  
It can be observed that the fitness value decreases sharply in the optimization process of the first 53 generations, and the best fitness value is found around the 50 th generation, and then the fitness value remains basically unchanged. The parameter optimization results of the Bayesian algorithm are shown in Table 3.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Rank** & **Feature** & **Score** \\ \hline
1 & DT & 865 \\
2 & NPHI & 661 \\
3 & CAL & 594 \\
4 & GR & 466 \\
5 & RT & 448 \\
6 & DEPTH & 429 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Importance scores of the parameters based on the LightGBM model.  
Figure 5: RMSE variation curve of different characteristic numbers.
# Small Object Detection in Optical Remote Sensing Images via Modified Faster R-CNN
## 5 Experiments and Results
### Comparative Experiment
As shown in Figure 7b-d, more small objects are able to be detected in the case that the stride is equal to 8, which suggests that producing higher-resolution feature maps simultaneously utilizing low-level features and high level features is very critical to enable us to detect small remote sensing objects. However, ships tend to dock in a complex scene such as a port while planes always line up on the airfield. These \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{**mAP (\%)**} & \multicolumn{2}{c}{**AP (\%)**} & \multirow{2}{*}{**Anchor Scale**} & \multirow{2}{*}{**Context**} & \multirow{2}{*}{**RR**} & \multirow{2}{*}{**BS**} \\ \cline{3-3} \cline{5-8}  & & & **Ship** & & & & & \\ \hline Baseline & 66.6 & 58.2 & 75.0 & \(\{128^{2}\,256^{2}\,512^{2}\}\) & & & \\ (stride = 16) & 67.1 & 59.5 & 74.6 & \(\{64^{2}\,128^{2}\,256^{2}\}\) & & & \\  & 68.1 & 60.1 & 76.0 & \(\{10^{2}\,40^{2}\,100^{2}\}\) & & & \\  & 73.5 & 71.0 & 76.0 & \(\{10^{2}\,40^{2}\,100^{2}\}\) & & & \\  & 74.1 & 71.7 & 76.5 & \(\{10^{2}\,40^{2}\,100^{2}\}\) & \(\surd\) & & \\  & 75.8 & 69.7 & 81.8 & \(\{10^{2}\,40^{2}\,100^{2}\}\) & & \(\surd\) & \\ Modified Faster R-CNN & 76.7 & 69.8 & 83.6 & \(\{10^{2}\,40^{2}\,100^{2}\}\) & & \(\surd\) & \\ (stride = 8) & 76.1 & 71.4 & 80.8 & \(\{10^{2}\,40^{2}\,100^{2}\}\) & \(\surd\) & \(\surd\) & \\  & 77.1 & 70.4 & 83.9 & \(\{10^{2}\,40^{2}\,100^{2}\}\) & & \(\surd\) & \(\surd\) \\  & 78.3 & 72.3 & 84.3 & \(\{10^{2}\,40^{2}\,100^{2}\}\) & \(\surd\) & & \(\surd\) \\  & 78.9 & 72.9 & 85.0 & \(\{10^{2}\,40^{2}\,100^{2}\}\) & \(\surd\) & \(\surd\) & \(\surd\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The results of modified Faster R-CNN on SORSI dataset. scenes often contain objects with similar geometric construction, such as a long straight line. These disturbances will cause false alarms on the detector, as shown in Figure 7. Furthermore, some objects are too small to be detected, resulting in misjudgments as illustrated in Figure 7b. At the same time, a few ground truths may be not annotated due to the manual annotation error as indicated in Figure 7a, which leads to false alarms as well.
## 4 Taxonomy of the Existing Multipath Routing Protocols  
Based on the discussed issues in Section 3.2, different applications demand for specific path selection and traffic distribution mechanisms.  
Figure 2 introduces a comprehensive classification of the multipath routing protocols proposed for wireless sensor networks. The suggested taxonomy classifies the existing multipath routing protocols into three main categories (_i.e._, alternative path routing, multipath routing for reliable data transmission, and multipath routing for efficient resource utilization), based on the employed path selection and traffic distribution mechanisms.  
Figure 3 shows the three main classes of multipath routing approaches and their improvements over time.
## 4 Results and Discussion
### Hybrid Learning of ANN  
In the first step of the GA-based ANN learning, the questionnaire data underwent postprocessing, and were formatted as the input and output data sets. The input nodes of the network were encoded into chromosomes in terms of real number and binary coding with respect to their nature. During this stage, the ANN initial weights were set using GA, and the minimum error was reached using a backpropagation algorithm. This temporary network design was not necessarily expected to produce favorable ANN model results, because its primary purpose was to find the minimum error of the network with respect to the selected input variables. The next step was to determine the optimum number of ANN hidden nodes, with the lower and upper boundaries fixed at 1 and 50, respectively. During the next stage, the number of hidden layers was increased by 1, and the previous steps were repeated. A new solution was obtained by the GA, and the NN used it to determine its fitness value, and this fitness was compared with that of the previous model. These steps were repeated until the minimum error was reached. The overall procedure is illustrated in Figure 4.  
After the most relevant inputs and an optimal number of hidden nodes were fixed, the GA was used to initialize the weights of the network. At this stage, the GA selected initial weight vectors for each individual in the population, which represented the ANN initial weights. The individual weights were then trained using the Lavenberge-Marquardt algorithm, previously selected input variables, and an optimal number of hidden nodes until convergence was obtained. The GA provided initial values for the ANN that were near the global minimum. Therefore, the final solution was expected to fall into or near the global minimum.  
Table 7 presents the performance of different GA-based ANN models, which were assessed using values of the mean squared error (MSE) and \(R^{2}\). The models were composed of different numbers of  
Figure 4: Flow chart of hybrid learning of GA-based ANN.  
hidden nodes in different numbers of hidden layers. The model built with \(28\times 38\times 19\) hidden nodes performed the best, and had an \(R^{2}\) of 0.843 and an MSE of 0.367 for Adj3 (like-dislike), which was superior to the conventional ANN model. Increasing the number of hidden nodes and hidden layers in conventional ANN does not typically improve the MSE and \(R^{2}\). Table 7 further reveals that the training of the ANN was relatively satisfactory when three hidden layers were employed; however, the training was less favorable and the training time was increased when four hidden layers were employed. Thus, three were selected as the most appropriate number of hidden layers.
###### Abstract  
The understanding of electrocatalytic activity and poisoning resistance properties of Pt and Pd nanoparticles, recognized as the best electrocatalysts for the ethanol oxidation reaction, is an essential step for the commercialization of direct ethanol fuel cells (DEFCs). In this paper, mono and bimetallic Pt and Pd nanoparticles with different atomic ratios have been synthesized to study their electrocatalytic properties for an ethanol oxidation reaction in alkaline solutions. The different nanoparticles were misochemically characterized by transmission electron microscopy (TEM), energy-dispersive X-ray spectroscopy (EDX), X-ray diffraction (XRD), and X-ray photoelectron spectroscopy (XPS). The electrochemical characterization was performed by cyclic voltammetry and chronoamperometry measurements. The electrochemical measurements indicate that Pt nanoparticles have much higher electrocatalytic activity for ethanol oxidation than Pd nanoparticles. The studies with bimetallic PtPd nanoparticles showed a significant impact of their composition on the ethanol oxidation. Thus, the highest electrocatalytic activity and poisoning resistance properties were obtained for Pt\({}_{3}\)Pd\({}_{2}\) nanoparticles. Moreover, this study demonstrates that the poisoning of the catalyst surface through ethanol oxidation is related to the prevalence of the acetaldehyde-acetate route and the polymerization of acetaldehyde through aldol condensation in the alkaline media.  
direct ethanol fuel cells; electrocatalyst; PtPd nanoparticles; antipoisoning property; acetaldehyde-acetate route +
Footnote †: journal: Applied  
Article
# Combined Data Augmentation on EANN to Identify Indoor Anomalous Sound Event
## 2 Materials and Methods  
Data augmentation represents a crucial process in data manipulation that involves making minor modifications to the raw data without changing its underlying structure. It serves to enhance the quality and diversity of the data. When applied to audio data, data augmentation techniques assist in preventing model overfitting and improving model generalization performance. The overall schematic diagram of the entire experiment is shown in Figure 1. In order to address the issue of dataset size and quality in indoor anomalous sound event recognition, we propose a combined data augmentation approach. The system input consists of training and testing datasets. We apply offline data augmentation to raw audio amplification for the training dataset and follow it with online data augmentation processing for the generated MFCC spectrogram. These data are then fed into a neural network recognition model for training.
###### Abstract  
Sentiment analysis is widely applied in the financial domain. However, financial documents, particularly those concerning the stock market, often contain complex and often ambiguous information, and their conclusions frequently deviate from actual market fluctuations. Thus, in comparison to sentiment polarity, financial analysts are primarily concerned with understanding the underlying rationale behind an article's judgment. Therefore, providing an explainable foundation in a document classification model has become a critical focus in the financial sentiment analysis field. In this study, we propose a novel approach integrating financial domain knowledge within a hierarchical BERT-GRU model via a Query-Guided Dual Attention (QSDA) mechanism. Driven by domain-specific queries derived from securities knowledge, QGDA directs attention to text segments relevant to financial concepts, offering interpretable concept-level explanations for sentiment predictions and revealing the 'why' behind a judgment. Crucially, this explainability is validated by designing diverse query categories. Utilizing attention weights to identify dominant query categories for each document, a case study demonstrates that predictions guided by these dominant categories exhibit statistically significant higher consistency with actual stock market fluctuations (\(p\)-value = 0.0368). This approach not only confirms the utility of the provided explanations but also identifies which conceptual drivers are more indicative of market movements. While prioritizing interpretability, the proposed model also achieves a 2.3% F1 score improvement over baselines, uniquely offering both competitive performance and structured, domain-specific explainability. This provides a valuable tool for analysts seeking deeper and more transparent insights into market-related texts.  
Keywords:explainable artificial intelligence (XAI); financial sentiment analysis; stock market prediction; query-guided dual attention; financial domain knowledge