esa-sceva/satcom-chunk-collection huggingface dataset:
# Effects and Mechanism of Plasma-Activated Medium on Angiogenesis of Vascular Endothelial Cells
## 3 Results
### Effects of PAM on Expressions of Key Proteins in VEGF-2 and MEK-ERK Signaling Pathways  
WB results showed that the VEGFR-2/GAPDH ratio in the control group, 15 s, 30 s, 45 s, 60 s and 75 s-PAM groups was 0.17 \(\pm\) 0.05, 0.27 \(\pm\) 0.09, 0.43 \(\pm\) 0.02, 0.36 \(\pm\) 0.04, 0.32 \(\pm\) 0.02 and 0.11 \(\pm\) 0.04, respectively. The p-MEK/MEK ratio was 0.31 \(\pm\) 0.06, 0.64 \(\pm\) 0.05, 0.69 \(\pm\) 0.08, 0.61 \(\pm\) 0.03, 0.43 \(\pm\) 0.06 and 0.19 \(\pm\) 0.10, respectively. The relative expressions of VEGFR-2 and P-MEK were significantly increased in 15 s and 30 s-PAM groups (\(p<0.05\)), significantly decreased in 75 s-PAM group (\(p<0.05\)), but remained unchanged in 45 s and 60 s-PAM groups (\(p>0.05\)) (Figure 7a,b).  
Figure 5: Effects of PAM on cell adhesion. The adhesion of cells in 15 s and 30 s-PAM groups was increased and the adhesion of cells in 45 s, 60 s and 75 s-PAM groups was significantly decreased. * compared with control group \(p<0.05\).  
Figure 6: Effects of PAM on secretion of VEGF and bFGF, * compared with control group \(p\) < 0.05.  
Figure 7: (a) Protein expression bands detected with Western blotting. (b) Statistical results, * compared with control group (0 s) \(p\) < 0.05.
# A Design Method for Gammachirp Filterbank for Loudness Compensation in Hearing Aids
## 4 Experiments and Discussion  
### Effect of Chirp Factor on Gammachirp Filter  
The amplitude-frequency response curve of the gammachirp filter at different center frequencies is shown in Figure 3a-c when c = 0, c = 2, and c = \(-\)2, respectively. It can be seen that the frequency-domain profile of the gammachirp filter shows obvious asymmetry. There is a peak drift relative to the curve of the gammatone filter (c = 0); that is, the maximum amplitude does not appear at the center frequency. In addition, filters with different center frequencies have distinct bandwidths. These characteristics are consistent with the features of the basilar membrane. On the other hand, the value of the chirp factor reflects the intensity of the input speech. Figure 3d shows the amplitude-frequency response curve of the gammachirp filter when the center frequency is taken as 2 kHz under diverse input speech intensities. It can be observed that as the intensity of the stimulus increases, the gain at the peak of the gammachirp filter also increases.
## 3 Methodology
### Data, Augmentation  
Due to the specificity of the climate and environment of each country, the dataset sources are still very limited in terms of agricultural product data. In the study, images of dragon fruit were collected from dragon fruit farms and packaging facilities and an image processing chamber with appropriate lighting to contribute to increasing the stability and accuracy of the model. The database contains images with many different shooting directions. The data comprised 5000 images for training and 1500 images for testing. The test data were the randomly taken pictures from the image-processing chamber to check the accuracy of the model when put into reality. Due to the limited data sources on agricultural products, the creation of data on dragon fruit is also a small contribution of this study. Some cases lead to overtraining model training. Some data augmentation methods were used in  
Figure 2: The framework of the self-learning algorithm in the dragon fruit recognition problem.  
this study, such as image rotation, gamma correction [14] and scale, which increased the number of databases for the model, helping to increase the accuracy of the model training to avoid over-training.  
Currently, two main types of Vietnamese dragon fruit are exported: red flesh and white flesh. Although there are more different types, their appearance is similar. To export to foreign markets, dragon fruit should be classified according to the different standards of each country; mainly, however, it is classified according to weight, volume, shape, defects and the number of broken dragon fruit scales (because this affects the quality of dragon fruit during storage and cleaning). In order to determine the characteristics of the dragon fruit, we use a combination of image processing and CNN. To determine the actual weight of the dragon fruit using the loadcell sensor with noise filtering, the dragon fruit is put into an image processing chamber to obtain an image of the dragon fruit in real time. In the image processing chamber, an RBG camera collects images of the dragon fruit from which to calculate, extract and compare the characteristics of dragon fruit and find defects, such as deep marks, cracks and white spots. The central processing unit calculates the group of dragon fruit by means of a conveyor system.
## 3 Results
### Provincial Scale Performance Evaluation
The final scatter plot (Figure 4f) presents the overall performance of the datasets in the entire study region, derived from the average values of selected RG stations and the number of grids for the datasets, depicting a highly significant association of the datasets with the observed data. Overall, IMERG7 has demonstrated the best agreement with the ground observations across all the provinces except GB, followed by MSWEP2.8, ERA-5, and CUR TS.
## 3 Results and Discussion
### Response Surface Methodology
#### 3.2.2 Drilling Torque
The relative error of regression equations that were obtained in the case of the flat drill is lower (between 2.14% and 2.65%) than the error obtained for the helical drill (Table 8). Since the desirability coefficient has a high value in the case of the helical drill, it is obviously that this kind of drill performs better in terms of energy consumption during drilling of plywood, than a flat tool. The obtained results are correlated with our previous study regarding the drilling of prelaminated wood particle boards [16]. \[\mathrm{E_{R}}=\frac{\left|\mathrm{Y}\ -\ \hat{Y}\right|}{\mathrm{Y}}\times 100 \tag{18}\]  
where \(\mathrm{E_{R}}\) is the relative error (%), \(\mathrm{Y}\) is the experimental value, and \(\hat{Y}\) is the predicted value. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Source** & **Sum of Squares** & **df** & **Mean Square** & **F-Value** & \multicolumn{2}{c}{_p-Value_**} & \multicolumn{1}{c}{**Observation**} \\  & & & & & **Prob \textgreater{} F** & \\ \hline \(X_{1}\)\({}^{2}\) & 0.0022 & 1 & 0.0022 & 1.84 & 0.196 & Not Significant \\ \(X_{2}\)\({}^{2}\) & 0.0055 & 1 & 0.0055 & 4.60 & 0.05 & Not Significant \\ \(X_{1}\)\(X_{2}\)\(X_{3}\) & 0.030 & 1 & 0.031 & 25.33 & 0.0002 & Significant \\ \(X_{1}\)\({}^{2}\)\(X_{3}\) & 0.00033 & 1 & 0.0003 & 0.28 & 0.60 & Not Significant \\ \(X_{2}\)\({}^{2}\)\(X_{3}\) & 0.00157 & 1 & 0.0015 & 1.31 & 0.27 & Not Significant \\ \hline R\({}^{2}\) & & & & 0.99 & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 7: _Cont._  
Figure 9: The influence of the drill tip angle and tooth bite on the thrust force: (**a**) flat drill (**a**,**b**) helical drill.
## 3 Synergy and Challenges of UAV-Assisted THz-VLC Systems  
### Synergy between THz and VLC in UAV Applications  
The combination of THz and VLC in UAV applications provides a synergistic approach that harnesses the strengths of both communication methods. THz communication offers extremely high data rates due to the large available bandwidth [5]. This makes it ideal for transmitting large volumes of data quickly from the UAV to a relay point. The requirement for a clear line-of-sight (LoS) for THz communication is less of an issue for UAVs operating at high altitudes, where there are typically minimal obstructions [8]. On the other hand, VLC, which also requires an LoS, can leverage the vast and unregulated visible light spectrum to provide high-speed communication indoors. VLC can be seamlessly integrated into existing LED lighting systems, providing dual functionality of illumination and data transmission within buildings [4].  
Moreover, UAVs can effectively utilize THz communication in outdoor environments, where they operate above many potential obstructions. This high-altitude advantage complements VLC's indoor efficiency, where controlled lighting conditions mitigate issues such as multipath fading and interference. The environmental adaptability of this dual-hop system is a significant advantage, with THz providing robust outdoor communication capabilities and VLC ensuring reliable indoor connectivity.
# Attention to Fires: Multi-Channel Deep Learning Models for Wildfire Severity Prediction
## 4 Experimental Results
### Multi-Channel Attention-Based Analysis
#### 4.6.2 Regression Backbone  
Moving to the second step network, it is important to recall the network operation, in order to understand how the first backbone errors can affect the final computation. In particular, the second network input is obtained as the entering satellite image masked by the first network binary map. Therefore, when an input perturbation results in an empty mask, as a full-of-zeros middle input from the intermediate step, we could imagine that the outcoming regression prediction would be dominated by noise. In the previous section we observed that the predictions obtained by perturbing RGB and VRE Multi-bands is expected to present for most of the inputs such a situation. As shown in Figure 9, the final  
Figure 8: Visualization of the all_occlusion results for the different Multi-bands. Rows (**a**) and (**b**) are two satellite examples from coral and cyan fold, respectively.  
prediction in the proposed examples are again empty masks. On the other side, the SWIR channels occlusion ends up in an all-burnt prediction. This behavior could suggests that, even though the second network gets most of the input information after the first backbone masking, the missing channels are still necessary to perform the regression.  
Summing up, the result obtained at the end of the second step is strongly related to the information shared through the first. Therefore the only occlusion operation we can expect to give acceptable results would be the AER and the NIR. The proposed examples effectively reproduce this effect.
## 3 Parameter Modeling and Error Analysis of the INS and GNSS Systems
### GNSS Error Analysis and Modeling
#### 3.2.1 Satellite Clock Error  
The core aim of GNSS navigation and positioning is to measure the time from satellite signal transmission to receiving by the user receiver, so the clock error will have a great impact on the positioning accuracy of GNSS. To correct the satellite clock deviation, a clock error model is established as follows:  
\[\delta t^{(s)}=a_{0}+a_{1}(t-t_{0})+a_{2}(t-t_{0})^{2}, \tag{29}\]  
where \(a_{0}\), \(a_{1}\), and \(a_{2}\) are the zero offset coefficient, frequency offset coefficient, and frequency drift coefficient of the satellite clock, and \(t_{0}\) is the reference epoch for correcting the satellite clock.
## 2 VCG Signal Database  
In this work, the VCG signals from the PTB diagnostic database ([[https://www.physionet.org/content/ptbdb/1.0.0/](https://www.physionet.org/content/ptbdb/1.0.0/)]([https://www.physionet.org/content/ptbdb/1.0.0/](https://www.physionet.org/content/ptbdb/1.0.0/)) (accessed on 20 June 2021)). were used to develop the proposed multi-channel multi-scale two-stage deep CNN approach [30,31]. The PTB database from Physionet comprises both 12-lead ECG and 3-lead VCG recordings of normal sinus rhythm (NSR) and various heart diseases, such as MI, hypertrophy, cardiomyopathy, bundle branch block, and dysnythymia, respectively [30]. Each VCG signal has been sampled at 1000 samples per second in the PTB database, and the amplitude value of each lead VCG varied between \(-\)16.384 mV to 16.384 mV. In this study, we have used 73 VCG recordings from 52 healthy controls (HC) subjects of PTB diagnostic database. Similarly, 99 VCG recordings from 148 subjects with MI pathology are used. For MI localization, 13, 20, 11, 21, and 13 VCG recordings from AMI, IMI, ALMI, ASMI, ILMI, and IPLMI classes, respectively, are considered. In the PTB diagnostic database [30,31], the number of VCG recordings for MI class is higher than the healthy class. A higher difference in the number of VCG instances between MI and healthy classes may cause the over-fitting problem during the training of the proposed MMDCNN model. Due to this reason, we have considered only 99 VCG recordings from the MI class in this work. Each VCG recording in the PTB diagnostic database contains three orthogonal leads (\(V_{x}\), \(V_{y}\), \(V_{z}\)), which represent the electrical activity of heart in three different planes [28].
## 4 Results
### Lithofacies Analysis
#### 4.3.2 Lithofacies Associations
Planar cross-bedding, parallel bedding, and trough cross-bedding were commonly observed in the coarser sandstone of the upper LA4 unit, resembling those in LA3 (Figure 8D). The one bed thickness of LA4 ranged from 1.50 m to 6.00 m.  
Interpretation. LA4 was interpreted as channel and mouth bar complex deposits of the delta front. Vertical stacking of mouth bar and subaqueous distributary channel deposits is commonly observed in delta front deposits [29,64,81]. The coarsening-upward trend and inverse grading in the lower LA4 unit resembled mouth bar deposits, whereas the frining-upward trend and normal grading in the upper LA4 unit were characteristic of subaqueous distributary channel deposits. In shallow-water deltas, strong fluvial influence often leads to the erosion of underlying mouth bars by subsequent channel scouring, facilitating the development of channel and mouth bar complex deposits [29,64,81]. L45: Subaqueous Interdistributary Mud Deposits  
Description. This lithofacies association was characterized by light grey and brownish-red, vertically stacked, thin-bedded, mud- siltstones. LA5 primarily consisted of lithofacies Src, Mpb, and Mm (Figures 7E and 8C). Ripple and lenticular bedding were predominantly developed in argillaceous siltstones, whereas mudstone exhibited horizontal bedding (Figure 8C). Bioturbation structures were occasionally observed. The one bed thickness of LA5 ranges from 0.10 m to 5.00 m.  
Interpretation. LA5 was interpreted as subaqueous interdistributary mud deposits of the delta front. The dark gray and brownish-red mudstones and silty mudstones indicate deposition in a low-energy, stagnant water environment [2,10]. Horizontal bedding and ripple bedding were commonly observed in LA5 [28]. This lithofacies association primarily developed in topographic lows between subaqueous distributary channels [24].
# An Improved Artificial Bee Colony for Feature Selection in QSAR
###### Abstract  
Quantitative Structure-Activity Relationship (QSAR) aims to correlate molecular structure properties with corresponding bioactivity. Chance correlations and multicollinearity are two major problems often encountered when generating QSAR models. Feature selection can significantly improve the accuracy and interpretability of QSAR by removing redundant or irrelevant molecular descriptors. An artificial bee colony algorithm (ABC) that mimics the foraging behaviors of honey bee colony was originally proposed for continuous optimization problems. It has been applied to feature selection for classification but seldom for regression analysis and prediction. In this paper, a binary ABC algorithm is used to select features (molecular descriptors) in QSAR. Furthermore, we propose an improved ABC-based algorithm for feature selection in QSAR, namely ABC-PLS-1. Crossover and mutation operators are introduced to employed bee and onlooker bee phase to modify several dimensions of each solution, which not only saves the process of converting continuous values into discrete values, but also reduces the computational resources. In addition, a novel greedy selection strategy which selects the feature subsets with higher accuracy and fewer features helps the algorithm to converge fast. Three QSAR datasets are used for the evaluation of the proposed algorithm. Experimental results show that ABC-PLS-1 outperforms PSO-PLS, WS-PSO-PLS, and BFDE-PLS in accuracy, root mean square error, and the number of selected features. Moreover, we also study whether to implement scout bee phase when tracking regression problems and drawing such an interesting conclusion that the scout bee phase is redundant when dealing with the feature selection in low-dimensional and medium-dimensional regression problems.  
artificial bee colony algorithm; feature selection; quantitative structure-activity relationship +
Footnote †: journal: _Algorithms_  
09 March 2021  
07 April 2021  
Published: 09 April 2021
# AR in the Architecture Domain: State of the Art
## 5 AR System
### Devices
#### 5.1.3 Computer  
The processing unit responsible for merging real and virtual data is essential in the AR system, affecting the level of reliability of human vision. In this domain, there is an essential distinction between AR and MAR (mobile augmented reality): the former refers to all fixed PCs, while the latter devices (i.e., laptops, mobile phones, tablets, HMDs, glasses) are portable systems with the capacity to process data, and with integrated sensors that can be moved with respect to the real environment.  
MAR devices are those most used in AR [98], and present specific pros and cons in their application [99]. The multiple cameras, inertial tracking system, and GPS make them particularly useful for outdoor AR applications. Indoors, instead, they rely on external anchors (marker or markerless) or positioning systems that exploit simultaneous localization and mapping (SLAM). This latter is an algorithm that maps the environment in which the user is located and tracks all movements [100]. AR apps containing this feature can recall physical and virtual objects within an environment according to users' movements. The main advantage of this technology is the ability to be used indoors, while GPS is only available outdoors. The project GEUINF [101] shows how to apply a markerless SLAM MR approach supported by the ARCore library to analyze indoor architecture facilities.  
A fundamental role of the devices is represented by their ability to superimpose real and digital data in real time. The increase in computing capacity, well described by Moore's law, has affected AR and MAR systems, especially the latter. Significant efforts to improve runtime performance for mobile applications have been carried out in the past decade, speeding up hardware, software, and rendering improvements. Mobile AR systems are certainly among the primary resources in AR prospects, developing some important AR components, such as multicore CPU parallelism, GPU computing, cache efficiency, memory bandwidth saving, rendering improvement, and computation outsourcing. The use of devices is closely linked to the capacity to access the data network.
# HS-YOLO: Small Object Detection for Power Operation Scenarios
## 2 Related Works and Methods
### Related Works
#### 2.1.3 High Resolution Representation  
When small objects in an image exhibit low resolution, the object details may lack clarity, and edge information can become hazy or unclear, posing challenges for object detection algorithms. Obtaining high-resolution images of small objects can enhance the details and clarity of these objects, making them more easily recognizable and locatable by the object detection algorithm.  
[PERSON] et al. [23] addressed the issue of small traffic sign detection by utilizing GAN (Generative Adversarial Network) methods, generating super-resolution representations of these small objects to narrow the gap between small and general objects, thereby enhancing the detection of the former. [PERSON] et al. [24] proposed a solution using GAN to restore high-resolution images for small objects that may be blurred in aerial images. However, the training of GANs requires an extensive dataset for effective adversarial training, imposing high requirements on the dataset size. [PERSON] et al. [25] introduced HRDNet, a method that employed a high-resolution feature pyramid network to more effectively capture both the fine details and contextual information of small objects. However, its detection capability for small objects in different categories or complex scenes may be weak, affecting its generalization ability. [PERSON] et al. [26] improved the input resolution of the detection network by adding a feature texture extraction module at the input stage. However, this method may not be suitable for objects with extremely low resolution.
## 3 Results
### Evaluation Index of the Prediction Model  
In this study, the square root mean square error (RMSE), mean absolute error (MAE) and correlation coefficient (R) are used to evaluate the prediction accuracy of the model, which are as follows:  
\[\text{RMSE}=\sqrt{\frac{\sum\limits_{i=1}^{N}\left(y_{i}-y_{i}^{\prime} \right)^{2}}{N}}, \tag{17}\]  
\[\text{MAE}=\frac{\sum\limits_{i=1}^{N}\left|y_{i}-y_{i}^{\prime}\right|}{N}, \tag{18}\]  
\[\text{R}=\frac{\sum\limits_{i=1}^{N}\left(y_{i}-\overline{y}\right)\left(y_{i }^{\prime}-\overline{y^{\prime}}\right)}{\sqrt{\sum\limits_{i=1}^{N}\left(y_{ i}-\overline{y}\right)^{2}\sum\limits_{i=1}^{N}\left(y_{i}^{\prime}- \overline{y^{\prime}}\right)^{2}}}, \tag{19}\]  
where \(y_{i}^{\prime}\) represents the prediction value corresponding to the \(i\)-th input data, \(\overline{y^{\prime}}\) represents the average value of prediction values, \(y_{i}\) represents the real test value corresponding to the \(i\)-th input data, \(\overline{y}\) represents the average value of real test values and \(N\) represents the number of prediction samples.  
Figure 10: Flow chart of the prediction structure response of the utility tunnel based on WO-DBN.
## 3 Methodology
### Data Preprocessing and Input Representation
The matching process employs a simple Multi-Layer Perceptron (MLP) for coarse-to-fine matching, avoiding the need for high-resolution feature maps. Given the dense feature map \(F\) or its subset \(F_{s}\subseteq F\), the MLP predicts pixel-level offsets \(o\) between matching features from an image pair \((11,I2)\). The prediction of the offsets \(o\) is conditioned on the matched feature pair \((fa,fb)\) and is formulated as in Equation (6):  
\[(x,y)=\arg\max_{i\in\{1,2,\ldots,8\},j\in\{1,2,\ldots,8\}}o(i,j) \tag{6}\]where \(o\in\mathbb{R}^{8\times 8}\) represents the logits of a probability distribution over the possible offsets. This refinement strategy allows for efficient pixel-level matching by reducing the search space, making it particularly suitable for resource-constrained settings. Hereby, we employed XFeat due to its focus on both accuracy and efficiency. The decoupling of keypoint detection and descriptor extraction, the use of multi-scale feature merging, and the integration of reliability maps all contribute to its robust performance in real-world applications. With its ability to perform real-time inference on limited hardware, XFeat provides a valuable solution for further application, such as in mobile robotics and augmented reality scenarios. ReTrackVLM processes input video frames by detecting objects using YOLOv8, extracting bboxes, keypoints, and descriptors for each detected object during the stage given in Algorithm 2. Additional features, such as overlap ratios and confidence scores, are calculated. These components are combined into embeddings, normalized for compatibility with YOLOv8, and serve as input for the subsequent stages. ```
0: Video frame \(f_{t}\), Pre-trained YOLOv8 detector, Keypoint extractor (XFeat)
0: Set of embeddings \(E_{t}=\{e_{1},e_{2},\dots,e_{n}\}\) for frame \(f_{t}\)
1: Detect objects in frame \(f_{t}\) using YOLOv8, obtaining bounding boxes bbxes\({}_{t}\)
2:for each bounding box \(b_{i}\in\) bbxes\({}_{t}\)do
3: Extract keypoints \(k_{i}\) and descriptors \(d_{i}\) using XFeat
4: Compute overlap ratios \(o_{i}\) with other bounding boxes
5: Compute confidence score \(c_{i}\)
6: Construct embedding \(e_{i}=[b_{i},k_{i},d_{i},o_{i},c_{i}]\)
7: Add \(e_{i}\) to \(E_{t}\)
8:endfor
9: Normalize bounding box parameters to \([0,1]\) for compatibility with YOLOv8
10:return\(E_{t}\)
```  
**Algorithm 2** Data Preprocessing and Representation