esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiments and Results Analysis
### Data Sources and Preprocessing
To ensure data quality and robustness, we implemented a multi-layered data-cleaning protocol: (1) segments with data completeness \(\geq\)98% were retained from the initial 132 road segments to exclude anomalous cases with excessive missing values; (2) sporadic missing values (<2% of retained data) were handled using forward-filling interpolation to preserve temporal continuity; (3) segments with pronounced travel time variability (coefficient of variation > 0.3) were prioritized to ensure discriminative patterns while mitigating potential GPS errors such as signal drift and multipath effects.
# Optically Transparent Nano-Patterned Antennas: A Review and Future Directions
## 2 Transparent Antennas: A Review  
### Nano Carbon  
#### 2.1.1 Graphene  
Graphene is one of the carbon isotopes and it has a structure in which carbon atoms gather to form a two-dimensional plane. Each carbon atom forms a hexagonal lattice, with the carbon atoms locatedat the vertices of the hexagon, called a honeycomb lattice. General methods to obtain graphene thin films include micromechanical exfoliation, epitaxial growth, chemical vapor deposition (CVD), and reduced graphene oxide [2; 3]. From an electrical point of view, graphene is a zero-gap semiconductor, or semi-metal, whose complex conductivity nature (frequency-dependent conductivity) allows the propagation of plasmonic modes at THz frequencies [4]. Therefore, a graphene antenna operates at the THz frequency using the negative imaginary conductivity characteristics of graphene, as shown in Figure 1.  
In Reference [6; 7], graphene-based antennas devised in microwave frequency and THz frequency are investigated. A graphene-based patch antenna resonating at the microwave frequency features a radiation efficiency of 8% in Figure 2a. On the other hand, using plasmonic propagation in the THz frequency, it is confirmed that the antenna efficiency is 60% due to transverse magnetic plasmon surface mode in Figure 2b. Owing to its negative imaginary conductivity, research on tunable devices, frequency selective surface, or reconfigurable antennas using graphene is now being conducted [8; 9].
## 2 Materials and Methods
### The Attention Mechanism with CNN-BiLSTM
#### 2.3.3 The Dual Attention Network (DA-NET)  
The dual attention network (DA-NET) [19] used after CNN as an attention mechanism that can capture feature dependencies found in the spatial and channel dimensions. As shown in Figure 9, DA-NET contains two parallel attention modules. For the position attention module, a self-attention mechanism is introduced to capture the spatial dependencies between any two positions of the feature maps. For the channel attention module, a similar self-attention mechanism is used to capture the channel dependencies between any two channel maps. Then, a certain channel dependence is calculated by a weighted sum of all the channel maps. The weight is decided by the similarity between the corresponding two channels. Finally, the outputs of these two modules are fused by a summation and processed via a convolutional layer to generate the output of DA-NET.  
Figure 8: The non-local-neural network with embedded Gaussian as function \(F\).  
Figure 9: Overview of the Dual Attention Network (DA-NET). The grey boxes represent the convolutional layers.
# Categorization of the Condition of Railway Embarments Using a Multi-Attribute Utility Theory
## Methodology
### Irregularities in Sub-Ballast and Embankment Fill  
Although [PERSON] and [PERSON] [34] note that degradation mostly affects the ballast layer, it is important to map and detect defects in the sub-ballast and subgrade layers. In this case, lower frequency antennas are used to locate anomalies in the sub-ballast and embankment fill (voids, animal burrows, water erosion channels, etc.). Reflections of the emitted waves occur when the signal reaches boundaries and/or anomalies at larger depths. The presence of irregularities results in specific signal trace peaks (Figure 4a). If the anomaly is in the form of a void within the investigated layers, for example, animal burrows, it shows in the radargram in the form of a hyperbola (Figure 4b). The parameters of these hyperbolas are commonly determined by utilization of generalized Hough transform or, recently, by utilization of neural network tools to reduce the analysis time [35].  
From the whole data set, the maximum number of deep irregularities (D\({}_{\text{max,IR,i}}\)) per section is assigned a quantification starting value (QSV) of 1, while 'no deep irregularities' is assigned a value 0. The rest are calculated as follows:  
\[\text{QV}_{\text{C5,i}}=\frac{\text{D}_{\text{IR,i}}}{\text{D}_{\text{max,IR,i} }}, \tag{5}\]  
where C\({}_{5}\) represents Attribute 5, QV\({}_{\text{C5}}\) represents the quantification value of Attribute 5, and D\({}_{\text{IR,i}}\) represents the number of deep irregularities for an observed section of a railway line, while D\({}_{\text{max,IR,i}}\) represents the number of deep irregularities for the section with the maximum number of irregularities along the investigated railway line. These values are determined based on a visual assessment of each radargram, by counting the number of hyperbolic phenomena for each investigated section.
## 4 Theoretical Background--Petri Net Theory  
### Introduction  
In this section, the fundamental theory of Petri nets with extension arcs are presented. Petri nets are selected as a process modelling and simulation tool, as they can describe efficiently the structure and the dynamic condition of systems typically considered discrete event and analyse their behaviour using well-defined tools. Petri nets provide strict mathematical, in parallel with intuitive graphical, expression that improves the understanding of the system.  
Today, Petri nets are considered a popular mathematical and graphical tool widely used for modelling, analysis, synthesis, performance evaluation, simulation and control of processes and systems typically considered a discrete event. They allow the representation and study of the structure as well as of the dynamic behaviour of systems and processes and have been proven to be a powerful tool for studying system concurrency, sequential, parallel, asynchronous, distributed deterministic or stochastic behaviour, resource allocation, mutual exclusion and conflicts [28; 29].  
Representation of the dynamic state of the modelled system enables the simulation of certain scenarios as defined from the definition of the initial state of the system and the state change rules (discrete event simulation). Popular fields of Petri nets include production and manufacturing systems, project management, computer networks, software development and engineering, traffic monitoring and control, power systems and robotic tasks. Except the typical engineering applications Petri nets have been used for studying chemical and biochemical processes, medical and healthcare tasks and cognitive, educational and learning procedures.
## 5 Experiment Result  
### Fine-Tuned YOLOv8 Object Detection Model's Performance Result  
As previously mentioned, multiple patients are represented by the usage of multiple LEDs, and those multiple LEDs are detected using the YOLOv8 object detection model. The fine-tuned object detection YOLOv8 model shows positive results. The model's performance is measured using several evaluation metrics in object detection. This study utilized several common metrics such as IoU in Formula (1) for mean Average Precision (mAP) in Formula (2), precision in Formula (3), and recall in Formula (4). The formulas are detailed as follows.  
\[\text{IoU}=\frac{B\cap B_{gt}}{B\cup B_{gt}} \tag{1}\]  
\[\text{Average IoU}=\frac{\sum_{i=1}^{N}\text{IoU}_{\text{i}}}{N} \tag{2}\]  
\[\text{Precision}=\frac{\text{TP}}{\text{FP}+\text{TP}} \tag{3}\]  
\[\text{Recall}=\frac{\text{TP}}{\text{FN}+\text{TP}} \tag{4}\]  
The mean Average Precision (mAP) formula is written as Equation (5)  
\[\text{mAP}=\frac{1}{n}\sum_{k=1}^{k+n}AP_{k} \tag{5}\]  
On an IoU of 0.5, the fine-tuned model achieved 0.96 mAP. Through the validation dataset, it is shown that the model has high confidence in detecting the LED matrix transmitter correctly. The other metrics, precision, and recall also show good results of 0.97 and 0.94, respectively, indicating the good performance of the trained weights. Based on this evaluation result, the model can detect the LED matrix for real-time implementation.  
Consequently, the explained simulation also demonstrates significant performance in detecting the LED matrix transmitters. As highlighted in each scenario, the detection model can detect both single and multiple LED matrix transmitters. In addition, at various angles, the object detection model locates the LED transmitter precisely. Furthermore, as later shown in the simulation results, on a smaller LED matrix, the object detection model can locate the LED matrix accurately.
# PCB Defect Images Super-Resolution Reconstruction Based on Improved SRGAN
## 3 Algorithm Improvement Proposed Algorithm
### Generator Improvements
In addition to incorporating the VIT network structure, the proposed algorithm employs the HFB (High-Frequency Block) in the generator step, the structure of which is depicted in Figure 5:  
As shown in Figure 5, the module uses a larger convolutional kernel because the variability of adjacent pixel points of PCB images is not very large, and the size of the convolutional kernel is increased so that the model can better extract the high-frequency information of the feature map in the shallow features, while DW convolution is used to reduce the number of model parameters, the introduction of the layer Norm layer can better suppress the overfitting phenomenon of the model, and the residual structure can prevent the network gradient explosion and enhance the gradient propagation, thus improving the model learning ability. The MLP network in the HFB module consists of the LINEAR module and the GELU activation function, which serves to complete the dimension-generation and then dimension-reduction operations to extract high-dimensional information, giving the model the ability to process information in parallel, and it can achieve a high degree of nonlinear global action to make the model have good fault tolerance and enhance the generalization ability of the model, and the drop path regularization module enhances the model robustness. The overall structure of the generator for the improved SRGAN-based super-resolution reconstruction network of PCB defect images in this paper is shown in Figure 6:  
As shown in Figure 6, the improved generator network structure consists of the LR image block obtained by downsampling as the input, which first enters a convolutional layer for shallow feature extraction and PReLU function for nonlinear mapping, and then the output feature map passes through the HFB module and VIT module in turn, and the last VIT module output enters the upsampling convolution module, and the upsampling convolution in the Pixel Shuffle module is responsible for recovering the image and upsampling role to improve the image resolution after the upsampling convolution goes through the CNN reconstruction layer to complete the whole generator, thus outputting the reconstructed super-resolution PCB defect image.
###### Abstract  
In structural deformation monitoring, traditional methods are mainly based on the deformation data measured at several individual points. As a result, only the discrete deformation, not the overall one, can be obtained, which hinders the researcher from a better and all-round understanding on the structural behavior. At the same time, the surrounding area around the measuring structure is usually complicated, which notably escalates the difficulty in accessing the deformation data. In dealing with the said issues, a digital image-based method is proposed for the overall structural deformation monitoring, utilizing the image perspective transformation and edge detection. Due to the limitation on camera sites, the lens is usually not orthogonal to the measuring structure. As a result, the obtained image cannot be used to extract the deformation data directly. Thus, the perspective transformation algorithm is used to obtain the orthogonal projection image of the test beam under the condition of inclined photography, which enables the direct extraction of deformation data from the original image. Meanwhile, edge detection operators are used to detect the edge of structure's orthogonal projection image, to further characterize the key feature of structural deformation. Using the operator, the complete deformation data of structural edge are obtained by locating and calibrating the edge pixels. Based on the above, a series of load tests has been carried out using a steel-concrete composite beam to validate the proposed method, with the implementation of traditional dial deformation gauges. It has been found that the extracted edge lines have an obvious sawtooth effect due to the illumination environment. The sawtooth effect makes the extracted edge lines slightly fluctuate around the actual contour of the structure. On this end, the fitting method is applied to minimize the fluctuation and obtain the linear approximation of the actual deflection curve. The deformation data obtained by the proposed method have been compared with the one measured by the dial meters, indicating that the measurement error of the proposed method is less than 5%. However, since the overall deformation data are continuously measured by the proposed method, it can better reflect the overall deformation of the structure, and moreover the structural health state, when compared with the traditional \"point\" measurements.  
structural engineering; overall deformation monitoring; perspective transformation; edge detection; close-range photogrammetry 2019; 9, 4532; doi:10.3390/appr9214532
## 4 Results and Discussion
Figure 7.2 Slug flow pipeline pressure distribution 10\({}^{\circ}\): (**a**) flow image in the pipeline at point A and \(P_{4}\) at time \(t_{1}\); (**b**) flow image in the pipeline at point A and \(P_{4}\) at time \(t_{2}\); (**c**) flow image in the pipeline at point A and \(P_{4}\) at time \(t_{3}\); (**d**) flow image in the pipeline at point A and \(P_{4}\) at time \(t_{4}\). Figure 7.3 Slug flow pipeline pressure distribution \(15^{\circ}\): (**a**) flow image in the pipeline at point A and \(P_{4}\) at time \(t_{1}\); (**b**) flow image in the pipeline at point A and \(P_{4}\) at time \(t_{2}\); (**c**) flow image in the pipeline at point A and \(P_{4}\) at time \(t_{3}\); (**d**) flow image in the pipeline at point A and \(P_{4}\) at time \(t_{4}\). Figure 7.4 Verification of slug flow pressure model with an inclination of \(20^{\circ}\). When the liquid accumulation volume is 300 mL and the gas velocity is 9.89 m/s, the slug flow in the pipeline with an inclination angle of 20\({}^{\circ}\) forms a cycle in four stages from \(t_{1}\) to \(t_{4}\). That is, upon completion of \(t_{4}\), \(t_{1}\) reoccurs, followed by \(t_{2}\) and then \(t_{3}\), as displayed in Figure 14a-d. With the backflow of the dissipative liquid slug and the formation of a new liquid slug at the elbow position, the liquid accumulation in the pipe returns to the state at time \(t_{1}\). However, the slug outflow process is the result of multiple cycles. The slug flow is rapidly discharged in this cycle until the liquid accumulation is insufficient to overcome the gravity and the interaction between the two phases, and eventually accumulates at the elbow of the pipe.
# Effect of Characteristics of Ground Motion on Seismically Induced Sliding Surface of Slopes
###### Abstract  
The seismic performance of slopes is typically evaluated with a pseudo-static method using equivalent horizontal load or with Newmark sliding block analysis. In both procedures, the definition of the potential sliding surface is a required input. The sliding surface has been reported to be marginally influenced by the input ground motion and, therefore, is most often assumed from a pseudo-static procedure. In this study, extensive series of two-dimensional dynamic nonlinear finite element analyses are performed to evaluate the sensitivity of the sliding surface on the slope geometry, soil strength parameters, and input ground motion characteristics. It is demonstrated that the sliding surface may vary with the intensity and frequency characteristics of the input motion. Slopes with inclination angle equal or less than 35\({}^{\circ}\) are shown to be marginally influenced by motion intensity if the mean period (\(T_{m}\)) \(<\) 0.3 s. However, slopes inclined at 45\({}^{\circ}\) are revealed to be more sensitive to the motion intensity and \(T_{m}\). For motions with \(T_{m}\) \(>\) 0.3 s, the sliding surface is demonstrated to widen with an increase in the intensity of the input ground motions. The degree of widening increases proportionally with an increase in \(T_{m}\). It is, therefore, recommended to derive sliding surfaces from a dynamic analysis for steep slopes.  
Keywords:seismic slope stability; sliding surface; finite element; dynamic analysis; nonlinear soil model +
Footnote †: journal: Applied Sciences
## 4 Towards Query Optimization to Maintain Bounded Congestion Continuously  
What can be said about the relationship between the problem of maintaining bounded congestion at a fixed target time and the more general problem of maintaining bounded congestion at all times?  
Figure 3: Illustration of Example 1. The trajectories of entities in \(A\) are in red. Those of entities in \(B\) are in blue.  
The following example shows that there are entity configurations, even ones that do not change over time, for which the query granularity required to maintain bounded congestion at all times is significantly smaller than that which suffices to obtain the same bounded congestion at a fixed target time.  
**Example 3**: _Consider a collection of \(n/2\) well-separated pairs, where the \(i\)-th pair has separation \(4i-1\). Uncertainty ply (and degree) can be kept at one at a deadline \(n\) time units from the start, by a scheme that queries entities with granularity one in decreasing order of their separation. On the other hand to maintain degree/ply one continuously, the \(i\)-th entity pair must be queried at least once every \(4i-1\) steps, so over any time interval of length \(n\), \(\Omega(n\ln n)\) queries are required._  
Our fixed target time results focus on the congestion of uncertainty regions of entities at one time \(\tau\). To describe continuously maintaining bounded congestion, we need a notation that describes an \(\mathcal{E}\)-configuration and its uncertainty at any time \(t\). Let \(Z(t)=(\zeta_{1}(t),\zeta_{2}(t),\ldots,\zeta_{n}(t))\) denote such a configuration at time \(t\) and let \(B_{i}(x,t)\), \(\sigma_{i}(x,t)\), and \(r_{i}(x,t)\) be the corresponding \(x\)-ball, \(x\)-separation, and \(x\)-radius of entity \(e_{i}\) in \(Z(t)\).  
The continuous strategy described in our companion paper [3] repeatedly queries individual entities just in time to maintain reasonably accurate information about their \(x\)-separation; that is, the perception of their \(x\)-separation is close to their true \(x\)-separation. Relying on the accuracy of this perception, the continuous strategy is able to schedule queries in a way that simultaneously (i) maintains the desired bound on congestion, and (ii) sustains the accurate perception of \(x\)-separation over time.  
In the following subsection, we outline how, once suitably initialized, the accurate perception of \(x\)-separation can be sustained using just-in-time queries based on perceived \(x\)-separation, leaving the details to the companion paper. This serves to motivate the second subsection in which we describe how the accurate perception of \(x\)-separation can be initialized at some time \(t_{0}>0\), from a state of unbounded uncertainty of entity locations at time zero, using query granularity that is competitive with any other initialization scheme. This is achieved by a modified version of the FTT-degree\([x\ +\ \Delta]\) scheme of Section 3, using higher query frequency and a more restrictive criterion than \((x\ +\ \Delta)\)-degree-safety.
# Adaptive Wavelet Coding Applied in a Wireless Control System
## 4 Obtained Results  
This section presents the results obtained through experimental tests and computational simulations of the models described previously for the considered control system. In order to analyze the impact of the processing time of the wavelet coding in a control loop that operates on the wireless link, the processing time of the wavelet coding and the performance of the controller with and without the coding technique were evaluated in the absence of channel errors. The purpose is to investigate if the insertion of the wavelet coding in a control loop generates some significant delay, which could affect the control of the system.  
Computational simulations were also carried out to evaluate the control system performance over the Rayleigh fading channel with the normalized Doppler in \(f_{D}T_{s}=0.002\) for different \(E_{b}/N_{o}\) values. During the simulations, \(10^{3}\) data packets were evaluated for each setpoint value.
## 2 Integrated Design Principles and Schemes  
Most space telescope designs use reflective or catadioptric optical systems. The reflective system can fold the optical path, it has a compact structure and is suitable for a long focal length optical system. In addition, the reflective system does not have the problem of chromatic aberration and secondary spectrum. The reflective system has relatively small restrictions on the aperture size, is more suitable for lightweight, and has a greater advantage in temperature control [11].  
Reflective systems include two types: coaxial reflective system and off-axis reflective system. Off-axis reflective systems can be designed with a larger field of view (greater than \(2^{\circ}\)), but relatively, their size and weight will also become larger. It is not conducive to the compression of the volume; in addition, the processing, testing and overall assembly and adjustment of off-axis reflective mirrors are more difficult and more costly. Although the field of view of the coaxial reflective system cannot be designed to be large, its overall size is relatively small, the stability of the opto-mechanical system is good, and the processing and installation are more convenient [12].  
The choice of the optical system structure should be as far as possible to choose a structure that is conducive to design, manufacturing and assembly on the premise of meeting the design indicators. Additionally, it is necessary to fully consider the cost factors of the camera in processing, testing, assembly and launching, etc., and reduce the volume and weight of the payload as much as possible.