esa-sceva/satcom-chunk-collection huggingface dataset:
# Hypergraph Analysis of Functional Brain Connectivity During Figurative Attention
## 5 Graph Construction
### Connectivity Graphs Based on Coherence
At \(f_{2}\), coherence decreases between the midline frontal (Fz) and right prefrontal (FP2) lobes, as well as between the left frontal (F3) and right prefrontal (FP2) lobes (lower left panel, Figure 6). These results suggest that the central cortex is a primary region of disconnectivity and that the reduction in prefrontal-central connectivity facilitates increased parietal-frontal interactions. For right-cube attention, a different pattern emerges. At \(f_{1}\), coherence decreases between the anterior frontal lobes (F7 and F8), while at \(f_{2}\), coherence decreases between the left parietal (P3) and midline central (Cz) lobes, as well as between the midline parietal (Pz) and midline central (Cz) lobes. Notably, the nodes (P3, Pz) and links to Cz with the greatest coherence reduction at \(f_{2}\) (lower right panel, Figure 6) coincide with those showing the  
Figure 6: Disconnectivity graphs based on anticoherence in the (**upper raw**) \(f_{1}\) and (**lower raw**) \(f_{2}\) frequency regions, associated with (**left column**) left-cube attention (\(\Delta CohL<0\)) and (**right column**) right-cube attention (\(\Delta CohR_{K}<0\)). Bold lines highlight the links with the largest negative coherence differences (Min \(\Delta Coh\)). Violet dots denote the nodes with the lowest sums of their link weights, indicated next to these nodes. right-cube attention (\(\Delta Corr_{R}>0\)). Bold lines highlight the links with the largest correlation differences (Max\(\,\Delta Corr\)), which are displayed next to the corresponding links. Orange dots denote the nodes with the highest sums of their link weights, indicated next to these nodes. By comparing Figures 5 and 6 with Figures 7 and 8, we observe a strong similarity between the connectivity patterns derived from cross-correlation and coherence measures. Specifically, the links showing the maximum increases and decreases in cross-correlation closely match those for coherence. This agreement between connectivity measures, based on both temporal and spectral analyses, provides strong validation
# A Nested U-Shaped Residual Codec Network for Strip Steel Defect Detection
## 3 Methodology
### Refinement Network  
In order to effectively utilize the features of defect objects, a deep supervision mechanism [33] was proposed to supervise the coarse saliency map generated by each URB of the decoder network, as shown in Figure 2. Furthermore, a residual refinement network (RN_1D) consisting of one-dimensional filters is proposed to further refine the last prediction map of the URB output, as shown in Figure 5. We choose the last prediction map because of its richer significant information.  
Here, RN_1D uses maximum pooling in the bottom-up way to adjust the feature size and reduce calculation complexity. The reason why we use the maximum pooling operation instead of average pooling is that small defects are easily lost in average pooling. The maximum pooling can solve this problem well and make the URB more concerned about significant defective areas. As with URB in Section 3.2, we use one-dimensional \(3\times 1\) and \(1\times 3\) convolutions instead of \(3\times 3\) convolutions, which greatly saves on computational costs. A dilated convolutional layer with a kernel size of \(3\times 3\) and a dilation rate equal to 2 was used as the bridge between the feature extraction layer (bottom-up pathway) and feature fusion layers (top-down pathway), which not only obtains the large receptive field, but also improves the detection accuracy. A batch normalization [34] and a ReLU [35]  
Figure 4: Illustration of the proposed residual U-shaped residual blocks (URB).  
activation function follows this dilated convolutional layer. Furthermore, non-overlapping max pooling is used for downsampling in the feature extraction layer (bottom-up pathway), and bilinear interpolation is utilized for the upsampling in the feature fusion layers (top-down pathway). Finally, we use the prediction map after the refinement network as the final prediction map of the NURCNet.
# User-BS Selection Strategy Optimization with RSSI-Based Reliability in 5G Wireless Networks
## 3 System Model and Problem Formulation
### Reliability Modeling  
The main objective of reliability modeling is to express the reliability of a given system in terms of the reliability measures of its constituent components. Consider a system \(Y\) that consists of \(n\) components. Each component can only have two distinct states: it can either be functional or be off. Let binary variable \(\pi_{i}\) be the state indicator of component \(i\) as follows:  
\[\pi_{i}=\begin{cases}1,&\text{if component $i$ is on,}\\ 0,&\text{if component $i$ is off.}\end{cases} \tag{1}\]A state of system \(Y\) is a description of the states of all its components; hence, \(\pi=\{\pi_{i}\}\) for \(i=1,\ldots,n\). Let \(\Pi\) be the set of all possible states of \(Y\). The structural function of \(Y\), denoted by \(f(\pi)\), is a binary function that indicates whether the system is working under a given state according to the following equation:  
\[f(\pi)=\begin{cases}1,&\text{$Y$ is functional,}\\ 0,&\text{$Y$ has failed.}\end{cases} \tag{2}\]  
On the basis of the above definitions, the reliability of \(Y\), denoted by \(R(Y)\), can be calculated using the following equation:  
\[R(Y)=\mathbb{P}[f(\pi)=1]=\sum_{\pi\in\Pi}f(\pi)\mathbb{P}[\pi]. \tag{3}\]
## 3 Explosion Test Program
### Damaged HFPC Panels
#### 3.2.2 PP-Steel HFRC Specim  
Since only Steel FRC panels could prevent panel structural damage, PP-Steel HFRC specimens were tested against the contact explosion. The post-test results are summarized in Table 5 and Figure 8. Better than PP FRC panels, the PP-Steel HFRC exhibited less server damage, with only craters and fine cracks being observed.  
For 0.5% Steel + 1.5% PP HFRC, 17 (front) and 22 (rear) radial cracks were found for S5-1. S5-2 showed 9 and 30 radial cracks for front and rear surfaces.  
For 1.0% Steel + 1.0% PP HFRC, constant damage phenomenon was achieved with 12 and 30 radial cracks for front and rear surfaces.  
For 1.33% Steel + 0.67% PP HFRC, only 8 (front) and 22 (rear) radial cracks were observed.  
For 1.5% Steel + 0.5% PP HFRC, 13 (front) and 26 (rear) radial cracks were found for S8-1 panel. S8-2 panel showed 8 and 29 radial cracks on the front and rear surfaces.  
The front crater diameter and depth are plotted in Figure 9a, and the rear crater diameter is shown in Figure 9b. The rear crater dimension was much larger than the front crater. It is interesting to note that the least damage of crater diameter and depth does not coincide with the same fiber hybrid, e.g., 1.33% Steel + 0.67% PP HFRC had the smallest crater depth while 1.5% Steel + 0.5% PP HFRC panel performed best in terms of front and rear crater dimension. However, in general the hybrid fiber mix with 1.5% Steel + 0.5% PP had the least damage.  
Figure 8: Damage mode of PP-Steel HFRC panels.
## 2 Methods
### Fire Behavior Simulation  
We used two methods to demonstrate how the temporal dynamic fuels layers influenced fire behavior and fire spread. First, we used the BehavePlus fire modeling [45] to calculate the fire behavior indices of National Fire Danger Rating System (NFDRS) [46; 47; 48], and we estimated variability in fire behavior under different fuel loads. Second, we used the Fire Area Simulator (FARSITE) [49] to simulate the fire spread behavior in the Murphy fire region. The general modeling approach was to simulate fire behavior and spread and determine levels of sensitivity to the different fuel loads as estimated from remotely sensed data.  
For the BehavePlus simulation, we calculated two indices commonly used for fire management decisions in the NFDRS analysis: the Energy Release Component (ERC) and Flame Length (FL). We used fuel moisture values representing very dry conditions (3% 1-h; 4% 10-h; 5% 100-h; 30% live herbaceous; 60% live woody) and low wind speed (5 mph). From our fuel load estimates, we created a number of custom fire-behavior fuel models for shrublands and grasslands:  
1. High: fuel parameters were set as the year with high live fuel load (2005)
2. Low: fuel parameters were set as the year with low live fuel load (2008)
3. Moderate: fuel parameters were set as the year of the Murphy fire (2007)
4. Default: fuel parameters were set as the default values from LANDFIRE  
Each custom fuel model file included three parameters specifying live herbaceous, live woody fuel, and 1-h dead fuel loads, which varied year-to-year. The default fuel models included the low-load, dry climate grass (GR2) fuel and moderate load, dry climate grass-shrub (GS2) fuel models that fuel parameters were set as the default values from LANDFIRE. FARSITE is a PC-based application software program that is widely used by different agencies for wildland fire management ([PERSON] 2004). For the FARSITE simulations, the spatial data of the fuel models and other surface parameters (elevation, topographic aspect, and slope) were downloaded from the LANDFIRE data portal and extracted for the Murphy fire region. LANDFIRE data have 30 m resolution and we resampled all the data to 1 km resolution to match our MODIS NPP-derived fuel load data.  
All FARSITE simulations were performed from July 16-23, 2007, using the historical weather data for fire planning analysis from the North American Regional Reanalysis (NARR) dataset [50]. Daily temperature, relative humidity, wind direction, wind speed, and precipitation data were extracted from the NARR dataset and used in the simulation. The initial fuel moistures were calculated from the weather data in FARSITE. All the simulations used the fire initialization point (Lat 42.16\({}^{\circ}\), Long \(-\)115.32\({}^{\circ}\)) recorded in the MTBS database as the only ignition.
# Reliability of Supply and the Impact of Weather Exposure and Protection System Failures +
## 3 Method
\]  
\[s\in\{a,b\}\]  
\[i\in\{1,\dots,l\}\]  
\[j\in\{1,\dots,l\}\]  
The system can now be represented as a transformed graph, where transmission lines are represented as vertices, and directed edges are possible paths of failure propagation due to protection system failures. Figure 3 gives a visual representation of the 4-bus system presented in Figure 1, where the adjacency matrices defines the directed edges between the source line and the target line in the graph. The adjacency matrix can then be further modified to incorporate the probability that a failure on a source line will propagate to a target line. From the initial equations in [17], it is clear that only FT3 and FT4 stem from adjacent lines and as such these fault types will receive the primary focus in this paper. FT3 is related to a failure on an adjacent line, which is not correctly cleared by the adjacent line's protection system. The probability of missing operation of the protection system on the \(s\) side for a given line is represented by the column vector \(p_{l}^{s,m}\). A matrix containing the probability that a failure propagates from the source line \(j\) to the target line \(i\) through the FT3 mechanism is created, named \(PT3\), by modifying the adjacency matrix (5). \[\text{PT3}=\sum_{s}\left[\text{A}^{s}\cdot\text{diag}(\mathbf{p}^{s,m})\right] \tag{5}\]  
Figure 3: Transformed 4-bus system. Transmission lines as vertices, edges as propagation paths. Labeled edges correspond to elements in the resulting adjacency matrices marked with a digit \(1\). PT3 only takes into consideration the properties of line \(j\)'s protection systems. The same probability matrix for FT4, a failure on the source line causing an unwanted non-selective tripping of the protection systems at the target line, _PT4_, must consider protection system properties of both the target line and the source line. The probability of line \(j\) successfully clearing a failure on its own line is included first in (6), followed by the probability of an unwanted non-selective tripping at line \(i\), incorporated through transposing the matrix. Note that the probability of spontaneous unwanted operation is represented by a single column vector since the target line response is considered side-independent, where \(p_{l}^{u}=P(p_{l}^{a,u}\cup p_{l}^{b,u})\). \[\text{PT4}=\sum_{s}\left[\left[\text{A}^{s}\cdot\text{diag}(1-\mathbf{p}^{s,m}) \right]^{T}\cdot\text{diag}(\mathbf{p}^{u})\right]^{T} \tag{6}\]  
Matrices representing the probability of different fault types propagating from a source line to a target line have now been established. These can be applied to calculate systems reliability indices for cut-sets. Two methods are presented here: an approximate method of system reliability using annual failure rates including protection system failures, and a method to calculate time series of probability of failure due to different fault types, which can be used together with [10] to calculate unavailability of cut-sets.
# Driving Fatigue Detection Based on the Combination of Multi-Branch 3D-CNN and Attention Mechanism
###### Abstract  
Fatigue driving is one of the main causes of traffic accidents today. In this study, a fatigue driving detection system based on a 3D convolutional neural network combined with a channel attention mechanism (Squeeze-and-Excitation module) is proposed. The model obtains information of multiple channels of grayscale, gradient and optical flow from the input frame. The temporal and spatial information contained in the feature map is extracted by three-dimensional convolution, after which the feature map is fed to the attention mechanism module to optimize the feature weights. EAR and MAR are used as fatigue analysis criteria and, finally, a full binary tree SVM classifier is used to output the four driving states. In addition, this study uses the frame aggregation strategy to solve the frame loss problem well and designs application software to record the driver's status in real time while protecting the driver's facial privacy and security. Compared with other classical fatigue driving detection methods, this method extracts features from temporal and spatial dimensions and optimizes the feature weights using the attention mechanism module, which significantly improves the fatigue detection performance. The experimental results show that 95% discriminative accuracy is achieved on the FDF dataset, which can be effectively applied to driving fatigue detection.  
driving fatigue detection; 3D convolution; channel attention mechanism; facial privacy protection Article
## 2 Materials and Methods
### Methodology
#### 2.3.3 Extreme Gradient Boosting (XGBoost)  
Extreme gradient boosting (XGBoost) is a new paradigm for Gradient Boosting Machines (GBMs), which improves the processing of databases through the optimization of decision tree algorithms [55]. The XGBoost algorithm solves the overfitting problem through regularization and built-in cross-validation, which improve the computational accuracy and allow for optimum speed. Furthermore, functions in the XGBoost algorithm are operated and computed automatically, and therefore XGBoost is extensively applied in applications such as classification [56] and estimation [57]. The XGBoost algorithm is derived from the concept of \"boosting\", which combines the forecasts of all \"weak\" learners with special training to foster \"strong\" learners. The expressions are as follows:  
\[f_{i}^{(t)}=\sum_{k=1}^{t}f_{k}(x_{i})=f_{i}^{t-1}+f_{t}(x_{i}) \tag{1}\]  
where \(f_{k}(x_{i})\) and \(f_{t}(x_{i})\) are the predicted values for the \(k\)-th and \(t\)-th iterations of the XGBoost model, respectively; \(f_{i}^{(t)}\) and \(f_{i}^{t-1}\) are the predicted values for the \(t\) and \(t\)-\(1\) iterations of the \(i\)-th sample; \(x_{i}\) is the input variable; \(k=[1,2,\dots,t]\), \(i=[1,2,\dots,n]\).  
To prevent overfitting problems without compromising the speed of the algorithm, the XGBoost algorithm can be derived as follows:  
\[Obj^{(t)}=\sum_{i=1}^{n}l\bigg{(}\overline{f_{i}^{(t)}},f_{i}^{(t)}\bigg{)}+ \sum_{i=1}^{n}\Omega(f_{i}) \tag{2}\]  
where \(Obj^{(t)}\) is the objective function; \(l\) is the loss function; \(\overline{f_{i}^{(t)}}\) is the true value of the \(t\)-th iteration of the \(i\)-th sample; and \(\Omega(f_{i})\) is the canonical term of the objective function, which is given by:  
\[\Omega(f)=\beta T+\frac{1}{2}\lambda\|\omega\|^{2} \tag{3}\]  
where \(\beta\) and \(\lambda\) are regularization parameters and \(T\) is the number of leaf nodes.
# Overview of Methods for Large-Scale RNA Synthesis
## 2 Methods of RNA Synthesis
### Ribozymes for RNA Production with Homogeneous Ends
#### 2.3.3 HDV-like Ribozymes  
An HDV ribozyme (approximately 85 nt) is found in the genomic and antigenomic strands of the hepatitis delta virus [122; 123]. Its secondary structure consists of four helical regions forming two coaxial stacks, which are linked by two single-stranded junctions (Figure 6C). The tertiary structure of the HDV ribozyme folds into a nested double pseudo-knot, constraining the overall shape and forming the active site of the ribozyme [123; 124]. The cleavage site is located in helix P1 and typically occurs at a guanosine residue. The HDV ribozyme is active in the presence of divalent ions such as Mg\({}^{2+}\), Mn\({}^{2+}\), or Ca\({}^{2+}\)[125]. The sequence upstream of the cleavage site is unrestricted. Therefore, the HDV ribozyme can be applied to generate 3\({}^{\prime}\) homogeneous ends for any RNA. On the other hand, highly structured  
Figure 6: Secondary structure models of (**A**) hammerhead ribozyme, (**B**) hairpin ribozyme, and (**C**) HDV ribozyme. Target RNA is marked with black line. Cleavage sites are indicated with red arrows.  
target RNAs can negatively influence the proper folding of the ribozyme domain, resulting in lower cleavage efficiency. In this case, incubating it at 65-70 \({}^{\circ}\)C, adding urea, or performing cycles of denaturation-renaturation can enhance the cleavage efficiency [126; 127; 128].  
The lack of a sequence requirement has resulted in the HDV ribozyme being widely used by structural researchers in the preparation of RNAs for crystallization. Examples include domain IV of the ribosomal 4.5S RNA from E. coli (PDB: 1 DUH), the nadA riboswitch (PDB: 7D82), and the constitutive decay element (CDE1) from human 3\({}^{\prime}\) untranslated region (PDB: 6 XWW) [118; 129; 130].
# Smart Viniculture: Applying Artificial Intelligence for Improved Winemaking and Risk Management
## 3 Applications of Artificial Intelligence in Viticulture
### Pest Management Using Drone Technology
utilized UAV-mounted sensors to identify esca infections in vine leaves and crops, employing vegetation indices to distinguish between healthy and infected plants, which enabled accurate early detection [53; 54]. Similarly, [PERSON] et al. applied hyperspectral imaging to detect leafroll virus in Cabernet Sauvignon vineyards. They used specific spectral bands and classification models to achieve precise infection identification, facilitating efficient vineyard management through early detection [55]. Furthermore, a study by [PERSON] et al., demonstrated the use of UAV multispectral imagery combined with AI to map the spatial variability of Botytis bunch rot (BBR) risk in vineyards. Conducted in Galicia, Spain, UAVs equipped with multispectral cameras captured high-resolution images to create Digital Terrain Models (DTM), Normalized Difference Vegetation Index (NDVI), Canopy Height Models (CHM), and Leaf Area Index (LAI) maps. These variables, combined with a Random Forest algorithm, generated accurate BBR risk heatmaps (R\({}^{2}\) > 0.7), enhancing disease management and reducing unnecessary treatments [56]. The ability of drones to integrate AI, sensors, and real-time data provides a unique advantage over manual methods by enabling proactive pest management. Drones can deliver precise, data-driven interventions that can adapt to evolving conditions in the vineyard, something not feasible with manual inspections. Looking forward, advancements in drone technology and AI are expected to further enhance the capabilities of pest management systems. Improvements in drone autonomy, battery life, and imaging resolution, coupled with more sophisticated AI models, will enable even more precise and efficient pest detection and control. The integration of real-time data from IoT sensors and the use of predictive analytics will allow for proactive pest management strategies, further reducing the reliance on reactive chemical treatments and supporting sustainable viticulture practices [57].
# Real-Time Identification of Look-Alike Medical Vials Using Mixed Reality-Enabled Deep Learning
## 2 Related Works
### Deep Learning for Object Detection in Industrial and Medical Applications  
Deep learning models, particularly YOLO (You Only Look Once), Faster R-CNN, and SSD (Single Shot MultiBox Detector), have been widely adopted in real-time object detection tasks. These models have been successfully applied in manufacturing environments to automate processes and improve operational efficiency. [PERSON] et al. [12] used Faster R-CNN to track assembly tools in real time, allowing for operators to quickly locate the required tools, reducing errors and improving workflow efficiency. Similarly, [PERSON] et al. [13] developed an AI-powered cable assembly system that uses deep learning to detect and position brackets accurately, thus streamlining industrial automation tasks. These studies showcase how real-time deep learning models can improve task efficiency by automating object detection and reducing manual intervention.  
In medical applications, deep learning-based detection models have been deployed for diagnostic imaging, surgical tool tracking, and drug identification. [PERSON] et al. [14] developed a YOLOv5-based model to identify medical instruments in surgical environments, highlighting the potential of CNNs to improve procedural precision. Furthermore, [PERSON] et al. [15] implemented YOLOv4 for real-time traffic sign detection, demonstrating that optimized deep learning models can improve detection speed and accuracy, even in complex and dynamic environments.  
However, in the identification of medical vials, deep learning-based solutions are still in their early stages. Traditional approaches, such as barcode-based scanning, remain the most commonly used method. However, barcode recognition suffers from limitations in real-world conditions, such as smudged labels, varying vial orientations, and obstructions, leading to higher rates of misidentification. This requires the integration of alternative computer vision-based techniques to improve the reliability of detection.
## 3 Results and Discussion
### Identification of Polyphenolic Compounds  
Chokeberry are fruits rich in various polyphenol fractions [3, 29, 32]. Table 4 indicates the qualitative identification of polyphenolic compounds found in chokeberry fruit. Compounds identification was performed by LC-PDA-ESI-MS/MS and quantitative analysis by UPLC-MS/MS. The extracts from black chokeberry fruits contained selected 27 compounds, including 11 flavonoids, 7 anthocyanins, 5 phenolic acids, 3 flavan-3-ols, and 1 flavanone. Identification of polyphenolic compounds was conducted on a comparison of their MS, MS/MS data, retention times, UV spectra of standards, and data available in the literature [27, 28, 29, 30, 33]. Compounds identified in the negative ion mode belonged to the group of flavonoids, hydroxycinnamic acids, flavanone, and flavan-3-ols. However, positive ionization was used to identify anthocyanins.