esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction: State of the Art
### Brushing Characteristics  
Brushing is a type of mechanical surface treatment with a rotary tool. Brushing tools are found across industries in applications predominantly involving deburring, edge rounding, glossing, and surface cleaning. Deburring is the typical purpose of brushing. The study by [PERSON] et al. [7] presented the complex impact of the conditions of brushing on deburring and the surface of 7075 Al alloys after milling, by analyzing the state of the edges (rounding, chamfering), edge roughness, microhardness, and internal stresses. As a result, brushing was shown to contribute to a significant increase in microhardness and internal stresses.  
Ceramic fiber brushes, the working part of which is made of alumina fibers, are relatively new tools on the market. Composed of myriad highly flexible brush microfibers (a single bristle consists of approximately 1000 microfibers), they adapt well to the shape of the workpiece.  
[PERSON] et al. [23; 24] examined the impact of brushing treatment on the microstructure, the mechanical properties, and the corrosion resistance of 1-mm-thick AZ31B Mg alloy plates under a varying feed and axial force applied by the brush against the workpiece. It was concluded that in the range of machining conditions adopted in the experiment, the grain size of the Mg alloy microstructure shows higher sensitivity to the feed rather than to the axial force applied by the brush. In addition, the yield strength and tensile strength increased slightly after brushing. The brushed surface was imparted with a high corrosion resistance, about four to seven times greater compared to the untreated one.  
In a different study [25], the question of improving the corrosion resistance of the reinforcing wire by means of the brushing treatment was undertaken. The wire moved between two opposite brushes--rotating at a speed of 8000 rpm with a feed speed of 300 mm/min--to intensify the process, which was additionally performed in two transitions. After the first pass, the reinforcing wire was rotated around its axis by 90\({}^{\circ}\) to obtain a uniform surface on the entire circumference of the specimen. Scale removal during brushing was shown to further contribute to enhancing the corrosion resistance. In addition, brushing is used to create a specific direction of the grain in the geometric surface structure, for a decorative effect, to enhance bonding in the adhesive joining process, or to impart specific properties of the surface layer [26; 27; 28].
## 4 Discussion  
The unrestricted proof regarding the application of bite wafers as an alternative to CADs for orthodontic pain alleviation was assessed and outlined by the authors of this review. Following the PRISMA guidelines and by applying strict eligibility criteria, the authors identified seven RCTs that were processed for data extraction (three RCTs [9; 20; 21]) compared bite wafers with Ibuprofen, three RCTs [2; 40; 41] compared bite wafers with Acetaminophen (Paracetamol), and one RCT [4] compared bite wafers with mixed OTC medications). Qualitative assessment of the individual study results indicated that in all RCTs, chewing of bite wafers was at least as effective as CADs for pain alleviation for up to 7 days after the placement of orthodontic archives. The present study's authors strived to pool all pertinent data from the included RCTs for quantitative analysis (meta-analysis). Since [PERSON] et al. [4] included a variety of OTC medications in the control group without providing separate pain scores for each type of medication, it was not possible to include this study for quantitative assessment.  
Moreover, since [PERSON] et al. [9] did not record numerical data [mean (\(\pm\)sd) pain scores], a meta-analysis regarding the comparison of bite wafer and Ibuprofen was accomplished by pooling data from two RCTs [20; 21]. The quantitative assessment was performed for RCTs using soft and hard bite wafers in [PERSON] et al. [21] and adolescents and adults in [PERSON] et al. [40] to maximize the pooled data. Furthermore, subgroup analyses were completed for each timepoint of pain assessment. Finally, a random effects model was used to pool the individual study results and revealed that the pooled difference in pain alleviation between the bite wafer and Ibuprofen groups for each of the timepoints did not significantly favor one of the groups. The pooled difference in pain alleviation between the bite wafer and Acetaminophen (Paracetamol) was not significant for the majority of timepoints assessed, except for day 3, which was a significant assessment favored the bite wafer. Critical appraisal of the available evidence indicates that the included RCTs had a moderate to high RoB, and the overall confidence in the meta-analysis results was very low. Based on individual and pooled study results, it appears that bite wafers are at least as effective as CADs for pain alleviation during the early stages (up to 7 days) of OT.
# Research on Plant Disease and Pest Diagnosis Model Based on Generalized Stochastic Petri Net
## 4 Simulation Analysis
### Stimulation Analysis of the Pest and Disease Diagnosis Model  
Simulation analysis was performed on the plant pest and disease diagnosis model to verify the effectiveness of the proposed method and to uncover key patterns in the evolution of pest and disease spread. By comprehensively analyzing place busy probabilities, place idle probabilities, and transition utilization rates, several critical nodes within the spread evolution process were identified: namely, information filtering (t\({}_{1}\)), mild infection (t\({}_{9}\)), moderate infection (t\({}_{10}\)), severe infection (t\({}_{11}\)), reaching treatment standards (t\({}_{12}\)), and treatment effectiveness assessment (t\({}_{21}\)). By altering the average firing rates of these transitions, the analysis explored their impact on the overall pest and disease propagation process and investigated the underlying mechanisms of each node within the diagnostic workflow. This approach contributes to optimizing the efficiency and effectiveness of the early warning system, enhancing the timeliness and accuracy of pest and disease control measures, and ultimately safeguarding the healthy development of plant cultivation.
# Immersive Digital Twin under ISO 23247 Applied to Flexible Manufacturing Processes
## 3 Materials and Methods
This includes the details of the physical entity, which are later used for creating animations and AR executions through the Unity game engine. The design includes external disturbance identification, monitoring, control, and process simulation models. All the 3D design features can be complemented with product manufacturing information (PMI) according to the ISO 16792 standard [35]. Several components are considered for the design of the virtual model, such as the dimensions of the geometric model, the surface of the physical model, equipment material, equipment parameters, and operating rules. The digital system requires a constant flow of information. This information is obtained through data input during the process simulation and automatically collected from the physical entity through sensors. The data are sent to and stored in the cloud, providing information about the process and the management of physical and virtual models, allowing for the analysis and control of the process based on these data. The interaction occurs through bidirectional communication interfaces using the MQTT protocol. For the AR interface, the virtual environment is exported to the Unity platform to be subsequently visualized, and control parameters are established using the Meta 2 AR Glasses device. Data transmission can be wired or wireless, depending on the type and importance of the data. These dynamic data collected from the physical model are used to modify the parameters of the virtual process model and are as follows: device identifier from which the data are collected, variable assigned to the device, communication time for data transmission, and values of the device parameters. Hand gesture applications have increased as technology advances, allowing for more natural ways to interact with computers. The data gloves used in this research are the Senso Glove: DK2 from Senso Device Inc1 [36]. The Senso Glove is a tool that can interpret hand and finger movements through sensors of inertial measurement units (IMUs) and wireless  
Figure 3: Immersive digital twin system development architecture. communication. It features eight inertial measurement units and two magnetometers in the wrist and palm, allowing it to accurately detect hand and finger movements without needing a camera. The eight sensors precisely measure the orientation of each part of the hand through a combination of accelerometers and gyroscopes [37]. The Senso Glove is used as a control tool for the system, as hand movements become the input for the system. Thanks to wireless communication, users can move their hands freely; additionally, the glove provides tactile feedback through vibration, enhancing user interaction [38]. It can read hand and finger movements and has been used in various applications. Therefore, in this research, it will be used for the immersive control of the process to analyze the interaction that this technology can provide in conjunction with the DT.
## 5 Region-Based Features
### Differential geometric features  
Polypoidal lesions may have a hemispherical shape or contain sections of their shape that are locally spherical. As a result, features derived from curvature of the mucosal surface are often highly discriminative, especially when distinguishing polypoidal lesions (part spherical) from folds (part cylindrical, or ridge-like). Differential geometric features can be computed from an explicit representation of the mucosal surface [12] or implicitly on the isosurface around a voxel [33; 34]. Following the example of [10], for each voxel \(\mathbf{p}\) in the region, we compute the volumetric shape index (SI) and curvedness (CV), defined as  
\[SI(\mathbf{p}) = \frac{1}{2}-\frac{1}{\pi}\arctan\left(\frac{k_{1}(\mathbf{p})+k_{ 2}(\mathbf{p})}{k_{1}(\mathbf{p})-k_{2}(\mathbf{p})}\right) \tag{3}\] \[CV(\mathbf{p}) = \sqrt{\frac{k_{1}(\mathbf{p})^{2}+k_{2}(\mathbf{p})^{2}}{2}} \tag{4}\]  
where \(k_{1}(\mathbf{p})\) and \(k_{2}(\mathbf{p})\) are the principal curvatures, computed directly from the image intensities. Every distinct shape, except for the plane, corresponds to a unique shape index. Most importantly for colon CAD, spherical shapes have a high shape index of 1.0, while ridge-like shapes have a lower shape index of 0.75; this enables the discrimination of polyps from folds. The curvedness represents the size of the curved object. For instance, a smaller and larger sphere might have the same shape index but different curvedness values. After computing the shape index and curvedness at each voxel in the region, we then compute the following nine statistical features of the region:  
* Mean, entropy, skew, standard deviation, and maximum of shape index
* Mean, entropy, skew, and standard deviation of curvedness
# Research on AGV Path Planning Integrating an Improved A* Algorithm and DWA Algorithm
## 2 Alternative Implementation of the A* Algorithm
### Traditional A* Algorithm  
The A* algorithm is a path planning algorithm that combines a heuristic function with a cost function, making it the most effective direct search method for solving the shortest path in static environments. It is also a highly effective algorithm for solving various search problems and is widely used in applications such as indoor robot pathfinding and game animation pathfinding [16]. The A* algorithm constructs a cost function based on the heuristic function, which considers both the cost of the distance from the new node to the initial point and the cost of the distance from the new node to the target point. The evaluation function of the A* algorithm is expressed as  
\[F(n)=G(n)+H(n) \tag{1}\]  
where \(F(n)\) represents the estimated cost from the initial node through node \(n\) to the target node, \(G(n)\) represents the actual cost from the initial node to node \(n\), and \(H(n)\) represents the estimated cost from node \(n\) to the target node. \(H(n)\) is also known as the heuristic function of the A* algorithm, which controls the speed and accuracy of the algorithm. In most cases, the Euclidean distance [17] is used as a heuristic function for the A* algorithm [18]. The distance formula is  
\[D=\sqrt{\left(X_{2}-X_{1}\right)^{2}+\left(Y_{2}-Y_{1}\right)^{2}} \tag{2}\]  
where \(\left(X_{1},Y_{1}\right)\) are the initial node coordinates and \(\left(X_{2},Y_{2}\right)\) are the target node coordinates.  
The A* algorithm utilizes an open list and a closed list to manage nodes to be expanded and those that have already been expanded, respectively [19]. When a node is searched, the adjacent nodes are added to the open list and their evaluation values are calculated using Equations (1) and (2). The node with the smallest evaluation value is then selected as the next node to be expanded and is added to the closed list. This process continues iteratively until the target node is added to the closed list, indicating that the A* algorithm has successfully found a path.  
The path generated by the traditional A* algorithm consists of grid centroids, which can lead to unnecessary twists and turns [20] resulting in an unsmooth movement path. Additionally, the lines connecting the grid centroids often become tangential to obstacle vertices, posing a collision risk for mobile robots. To address these issues, this paper introduces the grid obstacle rate and optimized sub-node selection to the traditional A* algorithm.
# Dermoscopy Images Enhancement via Multi-Scale Morphological Operations
## 2 Mathematical Morphology
### Geodesic Transformation and Reconstruction  
In geodesic transformations, two equally sized input images are used, denoted as marker and mask. The first image (marker) is modified by a morphological transformation and restricted below (geodesic dilation) or above (geodesic erosion) the second image (mask) [14].  
Let \(J\) and \(I\) be the marker and mask images, respectively, with the same domain (\(D_{I}=D_{I}\)). Geodesic dilation \(\delta_{I}(J)\) and erosion \(\varepsilon_{I}(J)\) can be defined as [14]:  
\[\delta_{I}^{(1)}(I) = \delta_{I}(J)\wedge I\text{ with }J(x)\leq I(x),\] \[\varepsilon_{I}^{(1)}(I) = \varepsilon_{I}(J)\lor I\text{ with }J(x)\geq I(x), \tag{4}\]  
where \(\wedge\) is the minimum between the pixels of \(J(x)\) and \(I(x)\) and \(\vee\) is the maximum between the pixels of \(J(x)\) and \(I(x)\). If we perform \(k\) times the geodesic dilation or erosion of \(J\) with respect to \(I\), we have to \(\delta_{I}^{(k)}(J)=\delta_{I}^{(1)}(J)[\delta_{I}^{(k-1)}(J)]\) or \(\varepsilon_{I}^{(k)}(J)=\varepsilon_{I}^{(1)}(J)[\varepsilon_{I}^{(k-1)}(J)]\).  
In practice, we can define geodesic reconstruction \(\rho_{I}(J)\) and dual geodesic reconstruction \(\rho_{I}^{*}(J)\) as follows:  
\[\rho_{I}(J) = \delta_{I}^{(i)}(I)\text{ with i such as }\delta_{I}^{(i)}(J)= \delta_{I}^{(i+1)}(J),\] \[\rho_{I}^{*}(J) = \varepsilon_{I}^{(i)}(J)\text{ with i such as }\varepsilon_{I}^{(i)}(J)= \varepsilon_{I}^{(i+1)}(J) \tag{5}\]  
Similar to the standard opening and closing, opening \(\gamma_{\rho}^{(m)}\) and closing \(\phi_{\rho}^{(m)}\) by reconstruction of an image \(I\) can be defined as follows [14]:  
\[\gamma_{\rho}^{(m)}(I) = \rho_{I}(\varepsilon_{mH}(I)),\] \[\phi_{\rho}^{(m)}(I) = \rho_{I}^{*}(\delta_{mH}(I)), \tag{6}\]  
where \(I\) is the mask image, \(\varepsilon_{mH}(I)\) and \(\delta_{mH}(I)\) are the markers image and \(m\) is the size of the structuring element.  
In the full experiment, the structuring element has the shape of a disk, and \(m\) indicates the size of the radius.
# An IoT-Based Encapsulated Design System for Rapid Model Identification of Plant Development
## 2 Rapid Plant Modeling System
* To conduct experiments across 5000 climate scenarios over a period of 2 years. * To obtain a relevant dataset of 6 mil. entries for the chosen wheat crop. * To apply machine learning algorithms to three different growth stages to obtain various use-case models of wheat crop development. * To structure the dataset and the models to be exploited for prediction of crop maturation, grain moisture, and optimization of pest treatments. The scheduled climate scenarios imply a single set of the following range of identifiers, together with their combination, for every BBCH stage:  
* Temperature [\({}^{\circ}\)C]: [4, 4.5, 5,..., 39.5, 40],
* Humidity [%]: [10, 15, 20,..., 95, 100],
* PPFD [umol/m\({}^{2}\)]: [0, 25, 50,..., 1725, 1750],
* Nutrients [pH]: [4, 4.5, 5,..., 9.5, 10]. Furthermore, each individual set of microclimate conditions (i.e., the climate scenario) is fixed around the real outside conditions, then permuted to positive and negative offset in order to reach all the required scenarios while minimizing the cost of operation. Therefore, e.g., outside light is exploited through the transparent barrier or dimmed by rotating the observed plant in the shaded area. The validity of the conditions is continuously monitored and maintained by control loops such that disturbances are compensated for or at least known, measured, and archived. The soil is conditioned equally on a much larger scale, out of which the small portions are taken and placed in the cups together with the seeds. The pH value is measured at the beginning of the process, then kept intact during the whole experiment period. Changing the pH value implies initiating a new experiment. Additionally, the number of plant groups (cups), with circa 5 plants per group, was doubled for the same climate scenarios. Once the planned experiments have been conducted, the entire collected dataset consisting of both chamber measurements and camera-based plant development identifiers will be made publicly available to facilitate future research on the topic of wheat plant development modeling. The web API was envisioned as being a form of a public portal, which serves as an interface for information and interaction with potential users who will be able to choose their climate and enter simple entry conditions such as sowing times, periods of treatment with different preparations, geographical location, soil indicators, etc., and as generate outputs consisting of indicators of the predictive analytic.
# A New Improved Multi-Sequence Frequency-Hopping Communication Anti-Jamming System
## 4 Jamming Utilization Method Based on IMSFH
### Frequency-Detection Network
#### 4.3.1 Network Structure and Parameter Updates  
The model structure is shown in Table 1. Since the input information in practical communication needs to be processed in real-time and the spectrum waterfall becomes more complex in a challenging jamming environment, we have referred to and improved the structure proposed in [31]. Specifically, we lowered the initial learning rate and introduced an early stopping mechanism during the model training. This not only reduces the network training time and ensures real-time communication, but also helps prevent overfitting, thus enabling the learning of reactive jamming rule in complex spectrum waterfalls. In pattern recognition, the frequency-detection problem is transformed into a multi-classification problem. Therefore, the loss function used in this paper is the Cross-Entropy loss function, whose expression is shown in (5).  
\[Loss(\hat{y},y)=-Min\sum_{i=l}^{\text{M}}y_{i}\log(\hat{y}_{i}) \tag{5}\]  
In (5), \(y\) represents the true frequency label value and \(\hat{y}\) denotes the predicted frequency value of the input spectrum waterfall \(S_{k}\). \(M\) is the total number of categories of the spectrum waterfalls. \(\hat{y}_{i}\) represents the predicted probability of the corresponding category \(i\), while \(y_{i}\) indicates the probability of the true label belonging to category \(i\). According to [32], the optimization objective of this paper is expressed in (6). Additionally, the weight update used in this paper is shown in (7), where \(w\) denotes the model parameters and \(\eta\) represents the learning rate.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Layer** & **Parameters** \\ \hline Input & Input Size: 150 \(\times\) 150 \(\times\) 3 \\ Conv2D & 8 filters (activation: ReLU), Kernel Size: 3 \(\times\) 3 (stride: 1) \\ Pooling & Max Pooling, Kernel Size: 2 \(\times\) 2 (stride: 2) \\ FC & 128 (activation: ReLU) \\ Dropout & Probability: 0.2 \\ FC & 128 (activation: ReLU) \\ Dropout & Probability: 0.2 \\ Output & 32 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Network structure.  
\[Min(Loss(\hat{y},y))=-Min\sum_{i=l}^{M}y_{i}\log(\hat{y}_{i}) \tag{6}\] \[\mathrm{w}_{t+1}=\mathrm{w}_{t}-\eta\frac{\partial(\mathrm{Loss})}{ \partial(\mathrm{w}_{t})} \tag{7}\]
## 3 Test Methods
### Three Calculation Methods of Dielectric Constants
#### 3.2.2 The Reflection Amplitudes Method  
The Reflection Amplitudes Method relies on the comparison of the amplitude from the surface of asphalt pavement (\(A_{1}\)) with the amplitude from a metal plate reflection on the pavement surface (\(A_{m}\)); this is because the metal plate offers 100% reflection to the GPR signals. The amplitude of the reflection from the metal plate was obtained from the data collected before the GPR detect using the same configuration applied during the test. The amplitude of the surface reflection and the time through the asphalt concrete layer were obtained from the 3 dr Examiner processing software. As shown in Figure 2, the amplitudes showcased as red lines were the boundary of different material layers. The curves on the right represent the actual radar signal and representation at a point on the radar image on the left. Real (red line) represents the waveform of the real part of the radar signal, while magnitude (green line) represents the amount of energy. This procedure was able to eliminate noise and detect significant signals. This signal recorded the corresponding time and depth, using reflections recorded from the metal plate as reference. With both reflection amplitudes, the dielectric constant was calculated with Equation (4) [1].  
The dielectric constant of the asphalt concrete layer can be calculated by the Reflection Amplitudes Method using Equation (4):  
\[\varepsilon_{AC}=\left(\frac{1+\frac{A_{1}}{A_{m}}}{1-\frac{A_{1}}{A_{m}}} \right)^{2} \tag{4}\]  
where, \(A_{1}\) and \(A_{m}\) denote the reflection EM wave amplitudes of the asphalt pavement surface and metal plate placed on the surface of the pavement, respectively.
## 3 Proposal for Smart Energy Management
### Software Description
#### 3.3.2. Electrical Market Data Collection  
The companies responsible for the operation of the electricity network provide information regarding the energy balance. They offer data about: the expected and actual energy demand; the real quantity of generated energy; and the energy generation structure, differentiating its origin. That is to say, it provides information about how much energy is generated by each source.  
On the other hand, electric companies advertise the price of energy providing information that is available online. So, the system can access these web services for obtaining economic information, which is also used to operate. In most cases, information can be obtained in real time, allowing a quick and effective response of the system.