esa-sceva/satcom-chunk-collection huggingface dataset:
# Advancements in EEG Emotion Recognition: Leveraging Multi-Modal Database Integration
## 3 Multi-Modal System EEG with Facial Expressions--Proposed Model
### Facial Expression-Based Emotion Recognition  
The accurate interpretation of facial expressions, which are key indicators of human emotions, is essential in developing a comprehensive emotion detection system. There are different studies and open-source systems using neural networks for facial expressions emotion recognition. The advanced DeepFace CNN a crucial neural network in facial expression analysis. DeepFace, a specialized and renowned CNN for face recognition and emotion detection, provides a reliable solution for this vital component of the emotion classification system. In the network structure of the DeepFace, a 48 \(\times\) 48 grayscale image is considered to be the input. The neural network was designed using a 12-layered architecture between Conv, Maxpooling, and Average pooling layers [48]. Also, face recognition will facilitate the avoidance of wrong facial expressions being fed into the system. Noted for its accuracy, DeepFace is a powerful tool for identifying and categorizing facial expressions, including angry, fear, neutral, sad, disgust, happy, and surprise. DeepFace is a valuable tool in the design of emotion recognition systems [48]. Using the DeepFace neural network, we can take advantage of the extensive development and training that have went into DeepFace's architecture as well as shorten the development time of our system by using the pre-trained CNN to extract and analyze facial features from images or video frames.  
We will examine in this study how DeepFace is incorporated into our system, the preprocessing processes required for its optimal performance, and the techniques used to combine its output with EEG topographical data-based network output for improved emotion recognition in more detail. The addition of DeepFace to our framework is an example of how specialized CNNs and deep learning combine to enhance the ability of our framework to capture human emotions with enhanced improved accuracy. The emotion model was implemented using a 12-layered architecture because the FER-2013 database (48 \(\times\) 48 \(\times\) 1) was grayscale and had a lower resolution. The DeepFace CNN model is composed of twelve layers, including an output layer with seven nodes representing the following emotions: angry, disgust, fear, happy, sad, surprised, and neutral. There are also five convolution layers, three fully connected layers, two average pooling layers, and one max pooling layer. The input size for this model is 48 \(\times\) 48 [48; 49].
###### Abstract  
This paper analyses the effect of the spatial assessment scale on irrigation performance indicators in small and medium-scale agriculture. Three performance indicators--adequacy (i.e., sufficiency of water use to meet the crop water requirement), equity (i.e., fairness of irrigation distribution), and productivity (i.e., unit of physical crop production/yield per unit water consumption)--are evaluated in five irrigation schemes for three spatial resolutions--250 m, 100 m, and 30 m. Each scheme has varying plot sizes and distributions, with average plot sizes ranging from 0.2 ha to 13 ha. The datasets are derived from the United Nations Food and Agricultural Organization (FAO) water productivity through open access of remotely sensed-derived data (the Water Productivity Open Access Portal--WaPOR) database. Irrigation indicators performed differently in different aspects; for adequacy, all three resolutions show similar spatial trends for relative evapotranspiration (ET) across levels for all years. However, the estimation of relative ET is often higher at higher resolution. In terms of equity, all resolutions show similar inter-annual trends in the coefficient of variation (CV); higher resolutions usually have a higher CV of the annual evapotranspiration and interception (ETIa) while capturing more spatial variability. For productivity, higher resolutions show lower crop water productivity (CWP) due to higher aboveground biomass productivity (AGBP) estimations in lower resolutions; they always have a higher CV of CWP. We find all resolutions of 250 m, 100 m, and 30 m suitable for inter-annual and inter-scheme assessments regardless of plot size. While each resolution shows consistent temporal trends, the magnitude of the trend in both space and time is smoothed by the 100 m and 250 m resolution datasets. This frequently results in substantial differences in the irrigation performance assessment criteria for inter-plot comparisons; therefore, 250 m and 100 m are not recommended for inter-plot comparison for all plot sizes, particularly small plots (<2 ha). Our findings highlight the importance of selecting the spatial resolution appropriate to scheme characteristics when undertaking irrigation performance assessment using remote sensing.  
Keywords:irrigation performance indicators; equity; adequacy; crop water productivity; spatio-temporal scale +
Footnote †: journal: Remote Sens.
## 3 Results and Discussion
### Acceleration Levels
Figure 5: Empirical cumulative distribution functions (CDFs) of peak acceleration values: (**a**–**c**) lateral direction with FM 0%, 10%, 20%; (**d**–**f**) longitudinal direction with FM 0%, 10%, 20%; (**g**–**i**) vertical direction with FM 0%, 10%, 20%. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Acc. Data (g, Peak)** & **FM** & **Location** & \begin{tabular}{c} **Max.** \\ **Acc.** \\ \end{tabular} & \begin{tabular}{c} **Acc. at 99\%** \\ **Occurrence** \\ \end{tabular} & \begin{tabular}{c} **Acc. at** \\ **95\% occ.** \\ \end{tabular} & \
\begin{tabular}{c} **Acc. at** \\ **90\% occ.** \\ \end{tabular} &
\begin{tabular}{c} **Acc. at** \\ **50\% occ.** \\ \end{tabular} \\ \hline \multirow{8}{*}{**Vertical**} & \multirow{4}{*}{0\%} & Floor & 2.33 & 1.40 & 0.87 & 0.67 & 0.31 \\  & & 1 st & 2.88 & 1.67 & 1.26 & 0.82 & 0.38 \\  & & 2 nd & 2.94 & 1.69 & 1.29 & 0.87 & 0.39 \\  & & 3 nd & 3.04 & 1.72 & 1.41 & 0.96 & 0.39 \\ \cline{2-7}  & \multirow{4}{*}{10\%} & Floor & 3.06 & 2.05 & 1.26 & 0.94 & 0.37 \\  & & 1 st & 3.79 & 2.18 & 1.46 & 1.14 & 0.41 \\  & & 2 nd & 3.88 & 2.62 & 1.64 & 1.14 & 0.42 \\  & & 3 nd & 3.98 & 2.82 & 1.66 & 1.24 & 0.45 \\ \cline{2-7}  & \multirow{4}{*}{20\%} & Floor & 2.82 & 1.52 & 1.01 & 0.77 & 0.32 \\  & & 1 st & 3.69 & 2.06 & 1.24 & 0.91 & 0.36 \\  & & 2 nd & 3.78 & 2.30 & 1.42 & 1.09 & 0.47 \\  & & 3 nd & 3.99 & 2.36 & 1.43 & 1.10 & 0.48 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: _Cont._It can be stated, based on the recorded data and CDFs, that higher acceleration levels can be observed with more free movement space. In the vertical direction, this means 1.21-1.51 times higher values in higher layers, with 1.44-2.81 times and 1.63-2.36 times higher values in the lateral and longitudinal directions, respectively, in comparison to the floor. Higher vertical acceleration levels in higher layers mean a higher risk of damage to the shipment. This is mainly due to the stacked unit performance of the shipment. For longitudinal and lateral orientations, the acceleration levels increase when more free movement space is left for packages; this is due to sudden and normal braking/acceleration events, as well as the effects of winding roads.
## 4 Results
### Coefficient Test of the MM-G2 SFCA Method
Based on the experimental results and drawing on the experience of distance decay coefficient parameter settings from the aforementioned literature, the distance decay coefficient in the subsequent experiments is set to 1. Comparative Analysis of the Traditional Gravity Model MM-G2 SFCA and the Standardized Gravity Model MM-G2 SFCA  
To validate the effectiveness of the standardized gravity model, the accessibility of public hospitals in Shenzhen was calculated using three main methods: the MM-2 SFCA, the traditional gravity model MM-G2 SFCA, and the standardized gravity model MM-G2 SFCA. The accessibility results obtained using the traditional gravity model MM-G2 SFCA method exhibited an order of magnitude difference in numerical range compared to other methods. Specifically, the accessibility of one area was significantly higher than all the other areas, as shown in Figure 7. This is primarily due to the extremely low time cost (very short distance) between the demand point and the supply point in that area, resulting in an excessively high weight in the gravity model. This phenomenon was not isolated; the issue of excessively high accessibility due to very short distances between supply and demand points was widespread in the traditional gravity model. The visual effect of the accessibility shown in Figure 7b is extreme because the MM-G2 SFCA method amplified the effect of the distance weight in Equation (6) on the supply-demand relationship. Figure 7c shows the one-hour accessibility results using the gravity-based 2 SFCA (G2 SFCA) method for the driving mode. The G2 SFCA method provides an improvement over the original 2 SFCA formula via the gravity model. Although the accessibility results were relatively normal, the uneven spatial distribution of high accessibility areas persists. In fact, this problem of the traditional gravity model is common in various gravity-based model 2 SFCA methods, although it is relatively less pronounced. The scatter density plot in Figure 8 shows the distribution characteristics of accessibility using the traditional gravity model MM-G2 SFCA method and comparing with the standardized gravity model MM-G2 SFCA method. The data distribution of the traditional  
Therefore, the standardized gravity model effectively addresses the issue of excessively high local weights commonly found in traditional gravity models while maintaining the role of the distance decay function, making it suitable for use as the distance decay function for the 2 SFCA method.
## 4 Experimental Objects and Analysis of Results
### Edge Image Extraction  
The traditional Canny algorithm and the improved Canny algorithm were applied to the concentricity detection of the first piston part using machine vision software, respectively. The extracted edges are shown in Figure 4a,b, and the inner and outer edge images were obtained by removing the interference points as shown in Figure 4c,d.  
In Figure 4, it can be seen that the improved Canny algorithm edge detection algorithm has a good edge detection effect compared to the traditional Canny algorithm, which can retain the edge information in a more complete way, and can suppress the interference information near the edge line.
###### Abstract  
The purpose of this study was to design a powerful fibrous sorbent for recovering precious metals such as Pd(II) and Pt(IV), and moreover for identifying its selectivity toward Pd(II) or Pt(IV) from a binary metal solution. For the development of the sorbent, polyacrylamideonitrile (PAN) was selected as a model textile because its morphological property (i.e., thin fiber form) is suitable for fast adsorption processes, and a high amount of PAN has been discharged from industrial textile factories. The PAN fiber was prepared by spinning a PAN-dimethylsulfoxide mixture into distilled water, and then its surface was activated through amidoximation so that the fiber surface could possess binding sites for Pd(II) and Pt(IV). Afterwards, by Fourier-transform infrared (FT-IR) and scanning electron microscopy (SEM) analyses, it was confirmed that the amidoximation reaction successfully occurred. The surface-activated fiber, designated as PAN-oxime fiber, was used to adsorb and recover precious metals. In the experiment results, it was clearly observed that adsorption capacity of PAN-oxime fiber was significantly enhanced compared to the raw material form. Actually, the raw material does not have sorption capacity for the metals. In a comparison study with commercial sorbent (Amberjet(tm) 4200), it was found that adsorption capacity of PAN-oxime was rather lower than that of Amberjet(tm) 4200, however, in the aspects of sorption kinetics and metal selectivity, the new sorbent has much faster and better selectivity.  
polyacrylonitrile; precious metals; adsorption; recovery; selectivity Article
## 3 Results and Analysis
### Influence Analysis of Various Factors
#### 3.3.2 Embedded Length  
The relative bond strength is defined as the ratio of the average bond strength to the tensile strength (\(\tau/f_{t}\)), and the relative embedded length is defined as the ratio of the embedded length to the height of the section steel (\(L_{e}/d\)). The relationship between the embedded length and the average initial bond strength, the average ultimate bond strength, and the average residual bond strength are shown in the following equations:  
\[\overline{\tau_{s}} =(-0.011L_{e}/d+0.440)f_{t} \tag{6}\] \[\overline{\tau_{u}} =(-0.070L_{e}/d+1.247)f_{t}\] (7) \[\overline{\tau_{r}} =(-0.041L_{e}/d+0.736)f_{t} \tag{8}\]  
As can be seen from Figure 8b, the bond stress decreases as the embedded length increases. The reduction effect of the average initial bond strength is not obvious, and the average ultimate bond strength decreases significantly.
# Time Classification Algorithm Based on Windowed-Color Histogram Matching
## 3 Proposed Time Classification Algorithm
### Time Classification
#### 3.2.1 Windowed-Color Histogram  
With the average of the annotated sky region in train dataset, we construct the reference color histogram of each time period. Figure 3 shows the histograms of dawn, day, and night class. In windowed-color histogram concept, we consider some dominant color parts more when compared with each other. As shown in Figure 4, we take a limited range (limited bandwidth) of the histogram centered on the average value of each color band.  
The windowed-color histogram of input image is computed with the pre-defined bandwidth. We compute the matching score with the windowed-color histograms of the references to classify the input image into each time period class, as shown in Equation (1).  
\[d_{i}(H_{1}^{i},H_{2}^{i})=F(H_{1\{-\frac{\text{BW}}{2},\frac{\text{BW}}{2}\} ^{i}}^{i},H_{2\{-\frac{\text{BW}}{2},\frac{\text{BW}}{2}\}}^{i}), \tag{1}\]  
where \(F(\cdot)\) is one of the histogram comparing methods, \(BW\) is a pre-defined range centered on the average value of each color, and \(i\) is an index of [blue, green, and red] bands.  
Figure 2: The results of the detected sky region using the Mask R-CNN [19].
## 3 Proposed Method  
### Preliminary  
Our method is an anchor-free one-stage detector based on CNN. Generally speaking, in the one-stage detector, the image is input into the backbone network firstly, and then the feature map is calculated. These feature maps can be defined as:  
\[\Phi_{n}=f_{n}(\Phi_{n-1})=f_{n}(f_{n-1}(...f_{1}(O))) \tag{1}\]  
In which \(O\) represents the original input image, \(f_{n}\) represents one layer in a network, and \(\Phi_{n}\) represents the feature map generated through the n-th layer. Generally speaking, with the increase of network depth, the size of the feature map will gradually decrease, while the channels of the feature map increase step by step. For general anchor-based detectors, the detection process can be formulated as follows:  
\[Dets=P(D_{n}(\Phi_{n},B_{n}),D_{n-1}(\Phi_{n-1},B_{n-1}),...,D_{n-k}(\Phi_{n-k},B_{n-k})),0<k<n \tag{2}\]  
In which, \(\Phi_{n}\) represents the n-th feature map, and \(B_{n}\) represents the preset anchor boxes in the n-th feature map. \(D_{n}\) represents the result of converting the information in the n-th feature map into detection results. \(P(.)\) represents the process of utilizing feature maps and then producing prediction results. Its function is to obtain regressed boxes from all feature maps and output the final regressed boxes after processing. \(D_{n}\) is obtained by the following process:  
\[D_{n}(\Phi_{n},B_{n})=\{cls_{n}(\Phi_{n},B_{n}),reg_{n}(\Phi_{n},B_{n})\} \tag{3}\]  
In which, \(cls_{n}\) represents the classification score in the n-th layer feature map, and \(reg_{n}\) represents the size and offset of anchor boxes regressed in the n-th layer feature map. Thus, it is easy to obtain the bounding boxes through these two items.  
The RPN proposed in Faster R-CNN is different from the Formula (2), because RPN does all the prediction work only in the last feature map. Thus, the process is as follows:  
\[Dets=D_{n}(\Phi_{n},B),n>0 \tag{4}\]  
In SSD, multi-scale feature maps are used, and their representations are the same as those in Formula (2). However, as a model of using multi-scale feature maps, FPN combines the information of the feature maps of different scales by combining bottom-up, top-down and horizontal connection. The process is as follows:  
\[Dets=P(D_{n}(\Phi_{n}^{\prime},B_{n}),D_{n-1}(\Phi_{n-1}^{\prime},B_{n-1}),...,D_{n-k}(\Phi_{n-k}^{\prime},B_{n-k})),0<k<n \tag{5}\]  
In which each \(\Phi_{n}^{\prime}\) has fused the information of each \(\Phi_{n}\). However, in the anchor-free method, because there is no need for preset anchor boxes, they can directly perform all the detection work only by processing the feature maps, thus it has a more concise structure as follows:  
\[Dets=P(D_{n}(\Phi_{n}),D_{n-1}(\Phi_{n-1}),...,D_{n-k}(\Phi_{n-k})),0<k<n \tag{6}\]
# Consensus-Based Distributed Target Tracking with False Data Injection Attacks over Radar Network
## 3 Distributed State Estimation Algorithm with False Data Injection Attacks  
In this section, a novel DSE algorithm is proposed to deal with the FDI attacks in radar networks. The algorithm consists of three main parts: injected data detection, attack parameter estimation and hybrid consensus. To estimate the attack parameter, every two radars are grouped together in the radar network. The determination for each radar of whether it is attacked is performed by using a real-time, residual-based \(\mathcal{O}^{2}\) detector. Then, each group runs a simple-yet-effective attack parameter estimation algorithm based on the detection results and pseudo-measurements of both radars. Finally, each group exchanges values with its neighbors and updates its own value by employing a hybrid consensus method to timely mitigate the FDI attack and achieve global consistency.
# Investigation on Numerical Simulation of VIV of Deep-Sea Flexible Risers
## 4 Results and Discussion
### Wake Vortex Characteristics Analysis  
In Figure 20, vortex diagrams are presented for nine different positions along the riser axis (Z/L = 0.029, 0.147, 0.265, 0.382, 0.500, 0.618, 0.735, 0.853 and 0.971). The diagram clearly shows that the vortex shedding modes at various positions along the riser axis were not the same and could mainly be categorized as 2S, 2P and P + S vortex shedding modes. The 2S mode was characterized by low intensity, and the energy it carried could only support vibration with lower amplitudes. Figure 20 depicts that the 2S mode occurred primarily in areas with smaller amplitudes such as the ends of the riser and the vicinity of the vibration stagnation point. On the other hand, the vortexes in the P + S and 2P modes were more intense, and they carried enough energy to support larger amplitudes which, in turn, strengthen the vortex and maintain the P + S and 2P modes. As Figure 20 demonstrates, P + S and 2P modes tended to occur in areas with higher amplitudes in the middle of the riser.
# Condition-Based Monitoring and Maintenance: State of the Art Review
## 2 Literature Review
### Analytical Hierarchical Approach  
The analytical hierarchical approach (AHP) is a structural approach that is one of the frequently used techniques. For example, (i) Bevilacqua and Braglia [42] developed an AHP in determining the maintenance strategy selection in an Italian oil refinery with five possible possibilities of preventive, predictive, condition-based, corrective, and opportunistic maintenance. The best maintenance policy was selected for each facility of the plants including sensitivity analysis to improve the effectiveness. Due to the subjectivity associated with AHP, it might fail to capture detailed uncertainties of the system. (ii) Li and Brown [43] showed an approach to minimize the weighted average system reliability index (WASRI) by ranking maintenance tasks on the basis of their marginal benefit-to-cost ratios, wherein the benefit was defined as improvement in WASRI. A prioritizing approach was used to achieve component and system reliability. (iii) Waeyenbergh and Pintelon [44] demonstrated a framework for integrating all of the available information within a company, including experience of maintenance workers to capture data through modern information and communication technology (ICT) by considering both of the computerized information and knowledge. However, the details about both the implementation and validation of the proposed framework were not described. (iv) Swanson [45] reported the relationship between maintenance strategies and performance and used factor analysis for developing maintenance strategies. Lastly, (v) Wang et al. [46] developed a two-stage prognosis model for optimum maintenance strategies that is based on a fuzzy AHP to evaluate different maintenance strategies (i.e., corrective maintenance, time-based preventive maintenance, condition-based maintenance, and predictive maintenance) for different types of equipment. In general, the applicability of AHP might be useful in selecting the maintenance process but limited in predicting the uncertainty associated with the system under surveillance.
## 4 Data Diversity of UAV
### Overview of UAV Dataset Uses
#### 4.1.1 Disaster Management  
UAV datasets are often used to figure out how much damage hurricanes, earthquakes, and floods have done. High-resolution images and videos captured by UAVs allow for precise mapping of affected areas and the identification of damaged infrastructure.