esa-sceva/satcom-chunk-collection huggingface dataset:
# A New Partitioned Spatial-Temporal Graph Attention Convolution Network for Human Motion Recognition
## 4 Optimization of ST-GCN Neural Networks
### CA Attention Mechanism Module
\[\text{f}=\delta\Big{(}F_{1}\Big{(}\Big{[}\text{z}^{h},\text{z}^{w}\Big{]} \Big{)}\Big{)} \tag{10}\]  
\[\text{g}^{h}=\sigma\Big{(}F_{h}\Big{(}\text{f}^{h}\Big{)}\Big{)} \tag{11}\]  
\[\text{g}^{w}=\sigma(F_{w}(\text{f}^{w})) \tag{12}\]  
where \([\cdot,\cdot]\) is a concatenation operation along the spatial dimension, \(\delta\) is a nonlinear activation function, and f is an i feature mapping. \(\sigma\) is the sigmoid activation function. The outputs \(\text{g}^{h}\) and \(\text{g}^{w}\) are expanded as attention weights, respectively. Finally, the output Y of the CA module can be written as in Equation (13). \[y_{c}(i,j)=x_{c}(i,j)\times g_{c}^{h}(i)\times g_{c}^{w}(j) \tag{13}\]  
In this study, the attention mechanism will focus on the extraction of human motion features. We propose the STGCN-CA attention model, which incorporates the CA attention into the ST-GCN. After optimizing the network, the STGCN-CA network structure is shown in Figure 4.  
Firstly, the STGCN-CA network performs global averaging pooling of the feature map output from the GCN layer along both the width and height directions. It is decomposed of two parallel one-dimensional feature codes to obtain feature maps in width and height directions. These feature maps are spliced together and sent to the shared convolution module for dimension reduction. Secondly, the feature map after batch normalization is sent into the Sigmoid activation function, and the feature maps with the same number of original channels are obtained by convolution according to the original height and width. Then, we use the Sigmoid activation function to calculate the attention weight in the height and width directions, respectively. Finally, the original feature maps are multiplied and weighted to produce the attention-weighted feature maps.
# Design of MIMO Antenna with Double L-Shaped Structure for 5G NR
## 5 Conclusions  
This paper proposes an eight-port broadband antenna array with high isolation for mobile terminals operating in the 5G NR frequency band. The broadband characteristics of the antenna are achieved by forming an L-shaped open slot by adding an I-shaped open branch on one side of the metal plate's rectangular slot. The proposed antenna array effectively covers the required frequency band (3.4-4.7 GHz), which corresponds to the N77/N78/N79 bands of 5G communication. The proposed antenna system offers suppressed mutual coupling (\(<\)-10 dB), high overall antenna efficiency (87%-96%), and an excellent envelope correlation coefficient (\(<\)0.08). This work also simulates the practical application scenarios of the proposed antenna array in the data transmission mode, revealing that the proposed design performs well in such scenarios. In addition, because the antenna has the characteristics of high frequency bandwidth efficiency, it can be used for 6G mobile communication and can better adapt to future communication development.  
Conceptualization, W.Y.; methodology, M.Y. and Z.W.; software, W.Y. and W.N.; validation, W.Y. and M.Y.; investigation, W.M.; writing--original draft preparation, W.Y.; writing--review and editing, W.Y. and W.M.; supervision, Z.W. All authors have read and agreed to the published version of the manuscript.  
Figure 15: Simulated parameters under DHM. (**a**) Reflection coefficient. (**b**) Transmission coefficient. (**c**) Total efficiency.  
**Funding:** This work was supported in part by the Anhui Provincial Natural Science Foundation of China under Grant no. 2108085 MF200, in part by the Natural Science Foundation of Anhui Provincial Education Department under Grant no. KJ2020A0307 and no. 2022 AH051583, in part by the High-level Talents Research Funding Project of West Anhui University under Grant no. WGKQ2022009, in part by the Academic Funding Project for Distinguished Top Talents of Colleges and Universities in Anhui Province under Grant no. gxbjZD2021088, and in part by Graduate Innovation Fund of Anhui University of Science and Technology under Grant no. 2021 CX2070.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Automated Detection of Misinformation: A Hybrid Approach for Fake News Detection
## 3 Methodology
### Datasets
#### 3.1.3 The WELFake Dataset  
The WELFake dataset, consisting of 72,134 news articles (35,028 real, 37,106 fake), merges four popular news datasets (Kaggle, McIntire, Reuters, BuzzFeed Political) to prevent classifier overfitting and provide more training data.
# Visual MAV Tracker with Adaptive Search Region
## 2 Model
### Adaptive SR  
To improve the tracking accuracy and robustness, some functions were included in the CSR model; these were shrink constraints (SC), size optimization (SO), and path prediction (PP).  
Figure 1: Procedure of tracking with search region (SR).  
Shrink constraints: If a portion of a target object is truncated in the SR, this truncation can cause tracking failure. Then, the estimated size becomes smaller than the true size, as depicted in Figure 2. In the CSR model, the SR size was controlled to be linearly proportional to the object size. Thus, if the estimation result is smaller than the true size owing to truncation, as shown in Figure 2b, the size of the SR decreases in the following frame, as shown in Figure 2c. The SR continues to shrink in subsequent frames, which results in tracking failure, as shown in Figure 2d.  
The SC of the SR was established to address this problem. First, the size of the SR is constrained to not be smaller than a threshold value. This constraint prevents the SR from shrinking beyond a certain size even if the object is truncated in the SR. Second, the size of the SR is constrained to not shrink if one or more edges of a bounding box are located on the edge of the SR in the previous frame. In the case of a moving object, when the image is cropped in the position of the previous frame, the object deviates from the center and is placed on the side. Finally, when the object is located near the edge of the SR, the shrinkage of the SR is inhibited. These schemes contribute to the robustness of the tracking algorithm.  
Size optimization: As previously mentioned, SR size is determined such that the ratio of SR width to object width remains constant across all frames. The accuracy of the FCNN and the failure rate depend on this ratio. Thus, SO was conducted considering the performance of the tracking algorithm.  
Path prediction: For the tracking of mobile objects, the location of the SR can be more accurately estimated when the dynamics of the objects are considered. Whereas CSR only considers the previous position of the object for determining the location of the current SR, the location of a trajectory predictive SR is determined based on the previous velocity of the object as well as its previous position. The PP model can determine the current location \(\mathbf{r}_{t}\) of the SR as  
\[\mathbf{r}_{t}=\mathbf{r}_{t-\Delta t}+c(\mathbf{r}_{t-\Delta t}-\mathbf{r}_{t-2\Delta t}), \tag{1}\]  
where \(\Delta t\) is the time interval between adjacent frames. Position \(\mathbf{r}\) is a two-dimensional vector in the images, and \(c\) is a correction coefficient that can be obtained by minimizing the tracking error. Because the position in the images is affected by the motions of the object as well as by the movements of the camera and distortion in the images, a correction coefficient has been included in Equation (1).
# Extracting Fingerprint Features Using Autoencoder Networks for Gender Classification
## 3 Materials and Methods
### Feature Extraction  
#### 3.3.1 Discrete Wavelet Transformation  
Wavelet has been extensively applied in feature extraction, soft-biometrics recognition, and denoising, etc. It decomposes an image into sub-bands containing frequency and orientation information to represent valid signals. Specifically, a fingerprint image is decomposed into four sub-bands at one level, namely low-low (LL), low-high (LH), high-low (HL), and high-high (HH), which is shown in Figure 3. Typically, the LL sub-band will be decomposed repeatedly since it is thought to represent the most energy, and k refers to the repeat times. If k is set, (3 \(\times\) k) + 1 sub-bands are available. The energy of each sub-band is calculated by Equation (1), which will be used as a feature vector for gender classification (\(E_{k}\)), where \(X_{k}(i,j)\) represents the pixel at the position i and j at the \(k\)th level. W and H represent the width and the height of the sub-band, respectively.  
\[E_{k}=\frac{1}{WH}\sum_{i=1}^{W}\sum_{j=1}^{H}|X_{k}(i,j)| \tag{1}\]
# Classification Models for Nitrogen Concentration in Hop Leaves Using Digital Image Processing
## 1 Introduction
Similarly, in [PERSON] et al. 's study [19], when used together with the color co-occurrence methods, texture-based hue, saturation, and intensity color features to identify grapefruit leaves (Duncan variety) under laboratory conditions, LDA achieved classification accuracies of over 95% in all classes using hue and saturation texture variables. However, models relying on intensity-based features showed a reduced classification capacity when classifying the front of the leaves, due to their pigmentation. Regarding post-harvest carrot processing, [PERSON] and [PERSON] [17] evaluated image processing techniques and discriminant analysis methods. Their study utilized 135 samples with different shapes and regularities (56 regular and 79 irregular) and extracted variables using DIP techniques. LDA and QDA were estimated, achieving classification rates of 92.59% and 96.30%, respectively. The performance of the quadratic analysis was comparable to that in similar studies on potatoes and cucumbers [18; 20]. Therefore, developing models to identify and classify potential nitrogen deficiencies in hop plants through leaf image analysis could contribute to the sector. In the long term, this classification tool could be advantageous for producers by enabling the acquisition of reliable data, facilitating crop management, and reducing costs. In addition, this technology is already widely used in other crops, such as soybean [21; 22] and maize [23]. The objective of this study was to evaluate the impact of image quality and model performance in classifying hop plants with low or high nitrogen concentrations, considering predictors derived from digitally processed leaf images obtained with a mobile phone camera (low resolution) and a digital scanner printer (high resolution). The prospect of accurate nutrient monitoring using widely available mobile phones could bring significant benefits in terms of cost and productivity in hop cultivation management.
## 4 Performance Evaluation of Improved SOSK-Means
### Datasets
#### 4.1.1 The Synthetic Datasets  
The synthetically generated datasets consisted of the A-datasets (three sets), Birch datasets (three sets), DIM (one set of a low dimensional dataset and seven sets of high dimensional datasets), and the S-generated datasets (four sets). The A datasets had an increasing number of clusters, while the Birch datasets had the number of clusters fixed at 100. The DIM datasets were characterized by well-separated clusters, with DIM002 having 9 predetermined clusters (with varying dimensions ranging from 2 to 15), while others had 16 clusters with dimensions ranging from 16 to 1024. The S1-S4 were two-dimensional datasets, characterized by varying spatial data distribution complexity with a uniform cluster of 15.
# Improving Medical X-ray Report Generation by Using Knowledge Graph
## 4 Experiments
### Datasets
#### 4.1.2 Open-I Dataset  
The Open-I dataset, collected at Indiana University Hospital, is a public radiology dataset containing 3955 radiology studies corresponding to 7470 frontal and lateral chest X-ray images. These radiological studies are related to one or more chest X-ray images. Each study reported impression, findings, comparison, and indication sections. Similar to the MIMIC-CXR dataset, we use multi-view chest X-ray images (frontal and lateral) and the indicator part as context input. In our approach, we follow the approach of the existing literature [2] and concatenate the impression part and the survey results part as the correct generated report.
# Incoherent Digital Holography: A Review
## 3 Principle of FINCH
### Mathematical Model of FINCH  
According to Figure 4, a wave from an object located at a position \((x_{1},y_{1},z_{1})\) passes through a lens with focal length \(f\), an interference filter, and a polarizer in sequence. After the polarizer, the object light is modulated by a reflection-type SLM. The s-wave component of the object light is perpendicular to the LC direction and thus is not modulated by the SLM. The p-wave component is parallel to the LC direction and thus suffers a quadratic phase modulation \(\phi(x,y)\) given by  
\[\phi(x,y)=\exp\left[\frac{-j\pi}{\lambda d}(x^{2}+y^{2})-j\phi_{ps}\right] \tag{8}\]  
where \(\lambda\) is the central wavelength after passing through the interference filter, \(d\) is the focal length of the quadratic phase distribution, and \(\phi_{ps}\) is the phase shift required for phase-shifting interferometry. If the object is a single point, a plane wave and a spherical wave with orthogonal polarization states to each other are generated on the image sensor plane. Therefore, an interference pattern, a FZP pattern, is formed on the image sensor plane by locating a \(45^{\circ}\) polarizer in front of the image sensor. Considering a 3D object \(U(x_{1},y_{1},z_{1})\), the image sensor records a pattern \(I(x,y)\), which is expressed as follows [36]:  
\[I(x,y;\phi_{ps})=I_{0}\bigg{[}C+\iiint U(x_{1},y_{1},z_{1})\exp\left\{\frac{-j \pi}{\lambda Z}\bigg{[}\left(x-\frac{x_{1}d}{f}\right)^{2}+\left(y-\frac{y_{1} d}{f}\right)^{2}\bigg{]}-j\phi_{ps}\right\}dx_{1}dy_{1}dz_{1}+C.C.\bigg{]}, \tag{9}\]  
where \(I_{0}\) is a constant, \(C\) is the zeroth-order diffraction wave, \(C.C.\) is the complex conjugate of the integral, and \(Z\) is defined as follows [36]:  
\[Z=\frac{(z_{3}-d)f^{2}-z_{1}(z_{2}d+z_{3}f-fd+z_{3}d-z_{2}z_{3})}{f^{2}-z_{1}( d+f-z_{2})} \tag{10}\]  
where \(z_{2}\) is the distance between the SLM and the lens with the focal length \(f\), and \(z_{3}\) is the distance from the SLM to the image sensor. The integral in Equation (9) is the cross-correlation between the object and quadratic phase distribution and is considered as incoherent summation of FZP patterns. The result indicates that the object light is recorded as a Fresnel hologram. After recording the incoherent hologram \(I(x,y)\), the object wave is extracted by the technique of phase-shifting interferometry [67]:  
\[\begin{array}{l}I_{0}\iiint U(x_{1},y_{1},z_{1})\exp\left\{\frac{-j\pi}{ \lambda Z}\bigg{[}\left(x-\frac{x_{1}d}{f}\right)^{2}+\left(y-\frac{y_{1}d}{ f}\right)^{2}\bigg{]}\right\}dx_{1}dy_{1}dz_{1}\\ =\frac{1}{4}\big{\{}I(x,y;\phi_{ps}=0)-I(x,y;\phi_{ps}=\pi)-j\big{[}I(x,y; \phi_{ps}=\pi/2)-I(x,y;\phi_{ps}=3\pi/2)\big{]}\big{\}}.\end{array} \tag{11}\]  
Figure 4: An optical setup of Fresnel incoherent correlation holography (FINCH).  
Finally, a 3D image of the object \(U(x_{1},y_{1},z_{1})\) is reconstructed by calculating the Fresnel diffraction integral.
# Transfer Learning for Operator Selection: A Reinforcement Learning Approach
## 2 Background and Related Work
### Adaptive Operator Selection  
Many NP-hard problems can be solved using evolutionary search techniques [12]. These are mostly stochastic optimisation algorithms that have already demonstrated their effectiveness in a variety of application domains. This is largely due to the parameters that the user can define based on the problem at hand. However, such algorithms are very sensitive to the definition of these parameters. There are no standard principles for an effective setting, so researchers from other domains rarely use those algorithms. One of the features that search algorithms with multiple alternative operators require is operator selection. In this paper, we focus on Adaptive Operator Selection (AOS) [1]. Since its introduction in the 1990s, many AOS approaches have been proposed in the literature, varying widely in various aspects such as the amount of knowledge to use from the algorithm's previous performance and whether or not it is a good idea to use previous quality in the learning process. In practice, Credit Assignment (CA) and Operator Selection (OS) are the two components that are used during the operator selection process [13; 14]. A definition based on fitness achievement over a solution is used in the CA component. OS, on the other hand, uses CA's captured knowledge to determine the quality of each operator before estimating its likelihood. Finally, based on the probability assigned to each operator, a selection strategy is used to choose an operator for evolving a parent. All the parents in an episode are evolved using the same selection strategy. As the algorithm learns more about the landscape, it moves the solutions in a specific search direction after a number of episodes.
## 4 Technological Background and Experimental Setup
### Test Setup  
The test setup includes the wearable device and the control circuit including the sensor, a smartphone, Bluetooth connectivity, internet connectivity, and a power source. The wearable has two parts, the hand and the thumb, and has provisions to fasten the thumb part to the thumb and the hand part to the forearm and the fingers. The wearable can be custom designed to use either on the right or left hand. Assistance is required to fasten the wearable to the hand during initial training. When powered up, the user establishes connectivity between the smartphone and the controller via Bluetooth and internet connectivity via the controller and Wi-Fi. We confirmed that a single current sensor is enough to track the amount of force used for grasping and gripping tasks. The control parameter angle of the wearable is achieved by sending pulses for tiny changes in position for each pulse till the actuator reaches the final position. This simultaneously can change the control parameter speed of the wearable device. The count parameter can be updated each time that the actuator reaches the desired position, thereby attaining the desired angle.
# Pollutant Emissions and Combustion Efficiency Assessment of Engines Using Biodiesel
## 3 Results and Discussion
### Experimental Test Results with Electric Generator  
The literature shows that real power is the main parameter to be measured in electric generators. This parameter is associated with combustion efficiency--if the generator is closer to its maximum power, combustion is more efficient, to quote. However, other studies have shown that the base product of the biodiesel has an influence on engine performance. An experiment was carried out with parameters similar to those used in this article, with the use of biodiesel derived from jojoba and carbon nanotubes of multiple walls to measure the performance of the engine. Similarly, engine preheating and variation of engine speed led to better burning of the fuel, in addition to similar parameters [35]. In comparison with this work, the main difference is the biodiesel raw material. Figure 8 shows the behavior of the electric generator; clearly, better combustion efficiency is observed when electric power increases.  
Figure 7: Experimental results with the monocylindrical engine.  
CO (carbon monoxide) emissions. Figure 9 shows the graph of (CO) emissions. The B10 blend shows a considerable increase in carbon monoxide compared to the other blends. The generation of exhaust gases due to the geometry of the combustion chamber has been reported in different scientific articles.  
Figure 8: Electric power vs. combustion efficiency of a electric generator.  
Figure 9: Carbon monoxide of the electric generator.  
Combustion efficiency (%Eff). Combustion efficiency is defined as the ratio of heat released by the fuel to the heat input by the fuel [36]. For combustion efficiency, a considerable difference in pure diesel (B0) and blends B0, B5, B10, B15, and B20 is observed in Figure 10. We can see that B0 tends to be around values of 76% to 77% of efficiency in contrast to the other blends that have efficiencies of 69% to 71%. This could probably be due to the geometry of the combustion chamber, time injection, pressure, temperature, type of fuel, and other factors.  
NO (nitrogen oxide) emissions. In Figure 11, nitrogen oxide emissions values are observed; for the B5 mixture, they are considerably increased. For mixtures B10 and B15, values with a tendency to decrease NO are observed.  
Figure 11: Nitrogen oxide from the electric generator.  
Figure 10: Combustion efficiency of the electric generator.  
In the previous figure, it shows a similar trend in NO emissions; for example, the B5 blend presents high values for NOx while lower NOx values are presented with the B15 blend.
## 4 Results and Discussion
### Underlying Hindrance Groupings
#### 4.2.1 Lack of Collaboration and Model Integration  
This critical hindrance grouping accounted for about 53% of the total variance of the 21 critical hindrances and included 10 hindrances. All the 10 hindrances could be related to the collaboration and model integration of BIM implementation in the building project. The widely-accepted definition of BIM proposed by the National Institute of Building Sciences (NIBS) stated that \"a basic premise of BIM is collaboration by different stakeholders at different phases of the life cycle of a facility to insert, extract, update or modify information in the BIM to support and reflect the roles of that stakeholder\" [56]. The hindrance with the highest factor loading was \"BIM model issues (such as ownership and management)\" (H34). Even BIM technology has been used by the designers and possibly the contractors, the technological process has been suffering from physical and information fragmentation in different stages of the project. The creation, integration, and use of digital design models would potentially raise many liability issues because little collaboration was built within the typical project team [12], such as the anxiety about providing wrong information by the designers [26] and about offering advice by the contractors. The professionals involved in the post-survey interviews observed that due to potential liabilities, the team members do not fully exchange data. For instance, as the principal role in the Singapore context, the architect may change the design frequently without informing other designers and the contractors, hindering the creation of a composite design and construction model, whereby, all the parties can work on it. Such issues urged the roles and responsibilities in the model management process to be established in standard contracts. Even so, the liabilities may not be solved without trust-based collaboration and proper risk sharing among the key stakeholders. As mentioned earlier, the building contracts in Singapore are still developed on the basis of the adversarial contractual system, leading to individualism and isolated working environment. Thus, three critical hindrances (H27, H28, and H33) that described the fragmented contractual relationships were related to the trust-based collaboration needed for BIM implementation among the team. \"Lack of sufficient evidence to warrant BIM use\" (H09) obtained the second highest loading. The post-survey interviewees highlighted that due to the lack of project-wide collaboration, BIM implementation appeared to remain in an early stage in Singapore; it is unrealistic to expect that in the short term, the project team can fully reap the benefits that BIM implementation brings. The lonely BIM adoption among the major stakeholders could also be attributed to the poor understanding of collaboration and data integration. Thus, \"poor understanding of off-site manufacture (OSM) process and its associated costs\" (H35) was also included in this grouping.