esa-sceva/satcom-chunk-collection huggingface dataset:
# YOLO-SE: Improved YOLOv8 for Remote Sensing Object Detection and Recognition
## 3 Materials and Method
### Wise-IoU Loss  
YOLOv8 uses Complete Intersection over Union (CIoU) [42] as the default loss-calculation method. CIoU builds upon Distance Intersection over Union (DIoU) by introducing the aspect ratio of the predicted bounding box and the ground-truth bounding box, making the loss function more attentive to the shape of the bounding boxes. However, the computation of CIoU loss is relatively complex, leading to higher computational overhead during the training process. Weighted Intersection over Union (WIoU) [45] proposes a dynamic non-monotonic focus mechanism, replacing IoU with dissimilarity to assess the quality of anchor boxes. It adopts a gradient gain allocation strategy, reducing the competitiveness of high-quality anchor boxes and mitigating harmful gradients caused by low-quality anchor boxes. This allows WIoU to focus on low-quality anchor boxes, ultimately improving the overall performance of the detector. WIoU comes in three versions, namely WIoU\({}_{\text{V1}}\), which constructs an attention-based bounding box loss, and WIoU\({}_{\text{V2}}\) and WIoU\({}_{\text{V3}}\), which build upon \(v1\) by adding gradient gain to the focus mechanism.  
The formula for calculating \(WIoU_{v1}\) is as shown in Equation (2):  
\[L_{IoU}=1-\frac{Bbox\cap Tbox}{Bbox\cup Tbox} \tag{1}\]  
\[L_{WIoU_{v1}}=L_{IoU}R_{WIoU} \tag{2}\]  
The calculation formula for Region-based Weighted Intersection over Union (R-WIoU) is as follows, as shown in Equation (3):  
\[R_{WIoU}=\exp\left(\frac{\left(\left(x_{Bbox}-x_{Tbox}\right)^{2}+\left(y_{ Bbox}-y_{Tbox}\right)^{2}\right)}{\left(w^{2}+h^{2}\right)^{4}}\right) \tag{3}\]  
Figure 6: The architecture of the Transformer Encoder module.  
The values of \(w\), \(h\), \((x_{Bbox},y_{Bbox})\), and \((x_{Tbox},y_{Tbox})\) are illustrated in Figure 7.  
To prevent \(R_{WIoU}\) from producing gradients that hinder convergence, w and h are separated from the computation graph. \(R_{WIoU}\) takes values in the range [1, e), significantly amplifying the importance of low-quality anchors. Loss Intersection over Union (LIoU), on the other hand, takes values in the range [0, 1], significantly reducing \(R_{WIoU}\) for high-quality anchors and focusing on the distance between the center points when Bbox and Tbox overlap.  
The dynamic non-monotonic focus mechanism uses \"outlyingness\" to assess anchor box quality instead of IoU, and it provides a wise gradient gain allocation strategy. This strategy reduces the competitiveness of high-quality anchor boxes while also mitigating harmful gradients generated by low-quality examples. This allows WIoU to focus on ordinary-quality anchor boxes and improve the overall performance of the detector.
## 1 Introduction
The former is an acceptor, A, which is charged negatively by accepting an electron (denoted by A\({}^{-}\)); the latter is donor, D, which is charged positively by giving an electron (denoted by D\({}^{+}\)). As the MCF rubber has A\({}^{-}\) and D\({}^{+}\) ions by electrolytic polymerization, it attains photovoltaic properties, as shown by the electrolytic polymerization reaction in the following section. The photosensitized dye molecules yield two types of MCF rubber solar cells: The dye displays photoexcitation or photoactivity when it is compounded with MCF rubber, which on vulcanization results in arid MCF rubber solar cell (dry type); when the dye seeps into the electrolytically polymerized MCF rubber, the photovoltaics are generated by a redox reaction, then the MCF rubber solar cell is wet (wet type). The former is closer to the concept of a perovskite-type solar cell [10] and is dealt with in this report, while the latter refers to a novel dye-sensitized solar cell [11; 12] or Gratzel-type solar cell [13] and will be discussed later. These principles and concepts are useful for the production of the MCF rubber solar cell suggested in this study. In the present study, the method of production of soft solar MCF rubber having photovoltaic and piezoelectric properties and using a chemical-physical model, was investigated. In the beginning, the principle of generation of photovoltaics in MCF rubber solar cell is described. Next, the photovoltage and current due to photoexcitation, based on p- and n-type semiconductors resulting from the electrolytic polymerization of MCF rubber, by doping or due to the dye, were measured. The effect of compression on the properties of the dry-type MCF rubber solar cell was studied. The photovoltage, photocurrent density, and piezoelectric sensing were measured to ascertain the photovoltaics due to irradiation by visible light under compression.
## 3 Results
### Mapping Disease Infestations with PL Satellite Imagery
Thus, the sensitivity of normalized two-stage VIs to disease infestations enabled the capture of more pathological and biophysical evidence in damage mapping. For example, the normalized two-stage variation for NDVI, which represents the crop growth impacted by dwarf infestation, had the most significant contribution to the classification for the rice dwarf class (VIP = 1.73). The normalized two-stage SAVI, TVI, RDVI, and MSR had the contribution to represent corresponding biophysical variations caused by the infestations, including canopy morphology and chlorophyll variation on the pixel-scale. The normalized two-stage TVI performed best in the PLS-DA approach for the rice blast class with a VIP value of 1.47. The normalized two-stage NDVI, RDVI, MSR, and SIPI were also important variables in the PLS procedure (VIP > 1). The normalized two-stage NDVI, SAVI, TVI, MSR, and SIPI had similar contributions for the glume blight classification. In contrast, the contributions of single-date VIs were not significant owing to a similar spectral response pattern could be found, such as the NDVI for both rice dwarf and rice blast.
## 1 Introduction
Finally, at a particular isofrequency state, it is also possible to limit the excitation to a single plane wave state in each band, i.e., \(k_{2}\) for \(e_{2,N\times 1}\) spatial mode band and \(k_{3}\) for \(e_{3,N\times 1}\) spatial mode band (as will be shown later) [42; 43; 44]. Nonetheless, we could choose to excite two plane wave states in each spatial band (as is done in [45]), and the total dimensionality of Hilbert space can be, in principle, increased by considering multi-level systems [46]. Hence, by exciting an isofrequency state \(\omega=\omega_{n}=\omega_{n^{\prime}}\) and by limiting a single plane wave state in each band (\(k_{n}\) and \(k_{n^{\prime}}\);\(k_{n}\
eq k_{n^{\prime}}\)), we create acoustic nonseparable states as:  
\[u_{N\times 1}=\left(Ae_{n,N\times 1}e^{ik_{n^{\prime}}x}+Be_{n^{\prime},N \times 1}e^{ik_{n^{\prime}}x}\right)e^{ik_{n^{\prime}}t} \tag{13}\]  
The quantities \(A\) and \(B\) will be shown later to be controllable complex amplitudes. Therefore, the objective of this paper is to demonstrate experimentally and numerically the realization of nonseparable superposition of isofrequency states in an array of elastically coupled waveguides. We need to have at least three coupled rods so that there are two non-zero spatial mode eigen values [42; 43; 44]. Hence, in the current study, we fabricated acoustic waveguides composed of up to three rods, i.e., \(N=1,\ 2,\ \text{and}\ 3\), and carried out physical and numerical experiment demonstrating the existence of (a) elastic modes taking the form of tensor products of spatial modes and spinor degrees of freedom, as well as (b) nonseparable linear combinations of these elastic modes with controllable amplitudes.
## 1 Introduction
This study entailed fundamental research on suspended sediment monitoring through underwater imaging and the development of a relatively safer and more economical method than the existing methods for suspended sediment measurement. Specifically, the difference in chromaticity values (RGB and gray scale) among three different concentrations was confirmed via underwater imaging by performing an experiment, and multiple linear regression models were developed to predict the turbidity based on the obtained values. The possibility of estimating the SSC from the predicted turbidity value using the turbidity-concentration relationship was subsequently confirmed.
## 4 Simulation Results and Performance Evaluation
### The Optimization Results of the Control Parameters for PI Control  
GA is capable of effectively searching the parameter space and finding the optimal control parameters. For the PI control based on pole placement, setting an expected peak time of 10 ms and an expected overshoot of 5% can yield satisfactory control performance. The control parameters for the PI controller are selected to match these expected results: \(k_{\mathrm{i}}=0.3453\) and \(k_{\mathrm{p}}=9.1459\times 10^{-4}\). The optimized PI parameters obtained through GA are \(k_{\mathrm{i}}=0.8908\) and \(k_{\mathrm{p}}=0.0049\). Figure 8 illustrates the optimization results for PI control parameters. Figure 8 shows the output voltage of the DAB. When the load suddenly decreases, the voltage drop value decreases from a maximum drop value of 920 V before optimization to 960 V after optimization, resulting in improved voltage stability.  
Additionally, the maximum drop rate decreases from 8.00% to 4.00% after optimization, indicating better stability. Figure 8 illustrates the evolution of the parameters. It contains the pre-optimization and post-optimization parameter values. It is worth noting that there are some concentrated values of local minima in the figure. These values can be used to achieve different stability requirements in different cases of operating conditions variation.  
The Frequency Response module in Simulink is used to perform frequency sweep on the closed-loop DAB system. Figure 8 presents the Bode diagram of the DAB system, showing an increase in phase margin from 123\({}^{\circ}\) (prior to optimization) to 126\({}^{\circ}\), further enhancing system stability. A comparison with the parameters designed using the pole placement method reveals that the control performance of the GA-optimized control parameters is better.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Value** & **Parameter** & **Value** \\ \hline \(V_{i}\
## 5 Results and Discussion
### Execution Time Overheads  
As a result of hardening, execution time overheads are generated, as shown in Figure 11. The horizontal axis presents all the hardened versions of each test program (P: Precise, and its approximations, e.g., A1, A2, etc.), whereas the vertical axis corresponds to the normalized execution time overhead. The time execution overhead is the relationship between the execution time of the hardened version and the original one. In this case, initially, the complete hardening of each version (SWIFT-R) was performed. That is, all registers used in each algorithm were protected.  
Figure 9: Standard deviation of unACE results for the Sobel filter (total).  
The Figure 11 shows that the highest overheads correspond to the hardened precise versions of each program, as expected. The highest overhead corresponds to the precise version of the Euler program (6.14\(\times\)), which indicates that hardening this algorithm implies a high cost.  
Moreover, it is observed that higher levels of approximation have lower overheads. For some approximate versions, the speed-up achieved in the approximate stage is so high that the overheads associated with hardening are less than 1\(\times\). This means that a hardened and optimized version of the original program is obtained. Such is the case of the second approximate version of the Euler logarithm (A2), with an overhead of less than 0.5\(\times\), and the versions A2 to A5 of the In2 program, with overheads of less than 0.8\(\times\).  
Figure 11: Execution time overheads of the hardened (precise and approximate) versions of the programs using SWIFT-R.  
Figure 10: Error vs. speed-up of approximated versions.
## 2 Materials and Methods
### One-Dimensional Convolutional Neural Network (I-D CNN)  
One-dimensional convolutional neural networks (1-D CNNs) are a variant of the traditional CNNs tailored for processing sequential data. Unlike their 2D counterparts, which are well suited for image data, 1-D CNNs excel in handling temporal sequences such as time-series data, audio signals, and any form of data where the sequence order is vital.
## 2 Materials and Methods
### Geometrical Model and Boundary Conditions  
In the flat-plate calculations, the computational domain of H = 0.26 m and L = 2 m, shown in Figure 2 below, was used. This geometry was modeled under the same conditions, taking the ERCOFTAC T3A experimental setup as a reference (ERCOFTAC). A no-slip condition was defined at the lower plate to represent viscous flow over a flat plate. A frictionless wall-boundary condition was defined at the upper plate. At the outlet, the pressure was defined as zero. Uniform inlet velocity of 5.3 m/s was defined at the inlet. These boundary conditions were also used to represent the ERCOFTAC T3A test case. In all compared turbulence models, GT, std. k-\(\epsilon\), RNG k-\(\epsilon\), std. k-\(\omega\), inlet-turbulence intensity (Tu), and length scale (LS) have been taken as 3.3% and 0.026 m. Inlet-turbulence intensities have been taken as 1%, 2%, 3.3%, 5%, and 10% for fixed-length scale of 0.026 m in the cases where the effect of turbulence intensity was examined. Wall-friction fluctuations, non-uniformities in uniform inlet velocity, and small residual gradients in the zero-pressure-gradient (ZPG) flow conditions in the represented test case have been neglected.  
Figure 2: Computational domain for flat plate.
# Cooperative Approaches to Data Sharing and Analysis for Industrial Internet of Things Ecosystems
## 4 Findings
### Services and Their Interplay  
A common theme in our findings is that each of the identified roles is tasked with the provision of a distinct set of goods and services. Most of these are defined in a formal binding manner in a service catalog that delineates the specified requirements. The exchange of goods and services is also tied to financial transactions: mostly to a defined price. Examples of such services were given in interviews CO2 and CO4; in interview CO2, the procurement and sale of goods in various forms were named a service, while in interview CO4, it was the physical provision of energy.  
For physical services, it was simple to identify the related financial transactions. Sometimes, accompanying services for the exchange of data and information were given. An interviewee of CO3 mentioned the provision of data from coffee machines. In interview CO8, the exchange of analyses in relation to benchmarking between different banks was mentioned as a supporting service. Notably, for data- and information-related services, the interviewees had difficulties in clearly stating the financial side. Future research should focus on the interplay between digital services and associated financial transactions.  
_Finding 5: Defining and tying financial transactions to nonphysical data-based services is identified as a challenge._
# Three-Dimensional Formation Control for Robot Swarms
###### Abstract  
This article addresses a distributed 3D algorithm for coordinating a swarm of autonomous robots (ARs) to spatially self-aggregate into an arbitrary shape based on only local interactions. Each AR has local proximity sensors for measuring the relative coordinates of its nearby AR. We assume that only a single AR, called the leader, can localize itself in global coordinate systems, while accessing the arbitrary user-specified 3D shape. We further assume that the leader has a communication ability superior to all other ARs so that the leader can directly send a communication signal to any other AR inside the shape. Our aim is to make the  
multi-hop communication link between any two ARs initially. This initial connection is crucial since every AR maneuvers based on local communication only, while not depending on global coordinate information.  
In the proposed formation controls, we locate ARs one by one, until the network entirely covers the arbitrary user-specified 3D shape. In the proposed 3D formation controls, each AR spreads the network by tracking an edge of the network.  
There may be a case in which the networked ARs entirely cover the user-specified 3D shape, but we want to add more ARs into the shape. In this case, we apply distributed rendezvous controls [9], while not moving the leader. Thereafter, the network begins clustering toward the leader. After the network begins clustering, coverage holes are newly formed in the 3D shape. Additional ARs are further located until they cover the holes in the shape entirely.  
Once a swarm of ARs are aggregated into a user-specified 3D shape, we can control the ARs so that they maneuver in a desired direction while maintaining the network connection. For the flocking maneuver, all ARs maneuver synchronized to the leader. We assume that the leader has a communication ability superior to all other ARs so that the leader can directly send a communication signal to any other AR inside the shape. Therefore, the velocity command of the leader can be transmitted to all ARs inside the shape in real time. The velocity of each AR is synchronized to that of the leader utilizing the communication command transmitted from the leader.  
As far as we know, our manuscript is novel in control of 3D formation for covering an arbitrary shape, such that network connection is preserved while each AR maneuvers based on local communication. We show that the proposed control yields reliable accuracy against significant AR failures and movement error. Utilizing MATLAB simulations, we demonstrate the outperformance of the proposed formation controls.  
The remainder of this article is as follows: Section 2 reviews the literature related to this study. Section 3 discusses assumptions and definitions utilized in our paper. Section 4 addresses the 3D formation controls. Section 5 addresses how to cover the sensing holes in the user-specified 3D shape. MATLAB simulations are presented in Section 6. Conclusions are addressed in Section 7.
# Optimization of Sensor Positions and Orientations for Multiple Load Case Scenarios
## 2 Materials and Methods
### Optimization Problem Formulation
#### 2.1.7 Simulating Temperature Measurements  
In the case of temperature distribution-based sensor placement optimization, the optimization methods focus on minimizing the difference between maximal in the structure (\(T_{max}\)) and detected temperature (\(T_{det}\)) under different load cases.  
In order to determine \(T_{det_{i}}\), no tensor operations are needed. First, a line that represents the sensor can be made using the sensor's position and orientation angles to compute direction vector \(\mathbf{d}\) and define the start and end points of the measurement path. The line is then discretized into several (\(v\)) points, and in these points, temperature values are interpolated from surrounding temperature data in the given load case, using Equations (10) and (11). Finally, to obtain \(T_{det_{i}}\) the interpolated temperatures along the line are averaged as follows:  
\[T_{det_{i}}=\frac{1}{l}\int_{\mathbf{p_{1}}}^{\mathbf{P_{2}}}T_{q}\mathrm{d} q\approx\frac{1}{v}\sum_{g=1}^{v}T_{g} \tag{26}\]
###### Abstract  
This paper presents the use of noncontact ultrasound for the nondestructive detection of defects in two plastic plates made of polyamide (PA6) and polyethylene (PE). The aim of the study was to: (1) assess the presence of defects as well as their size, type, and orientation based on the amplitudes of Lamb ultrasonic waves measured in plates made of polyamide (PA6) and polyethylene (PE) due to their homogeneous internal structure, which mainly determined the selection of such model materials for testing; and (2) verify the possibilities of building automatic quality control and defect detection systems based on ML based on the results of the above-mentioned studies within the Industry 4.0/5.0 paradigm. Tests were conducted on plates with generated synthetic defects resembling defects found in real materials such as delamination and cracking at the edge of the plate and a crack (discontinuity) in the center of the plate. Defect sizes ranged from 1 mm to 15 mm. Probes at 30 kHz were used to excite Lamb waves in the slab material. This method is sensitive to the slightest changes in material integrity. A significant decrease in signal amplitude was observed, even for defects of a few millimeters in length. In addition to traditional methods, machine learning (ML) was used for the analysis, allowing an initial assessment of the method's potential for building cyber-physical systems and digital twins. By training ML models on ultrasonic data, algorithms can distinguish subtle differences between signals reflected from normal and defective areas of the material. Defect types such as voids, cracks, or weak bonds often produce distinct acoustic signatures, which ML models can learn to recognize with high accuracy. Using techniques like feature extraction, ML can process these high-dimensional ultrasonic datasets, identifying patterns that human inspectors might overlook. Furthermore, ML models are adaptable, allowing the same trained algorithms to work on various material batches or panel types with minimal retraining. This combination of automation and precision significantly enhances the reliability and efficiency of quality control in industrial manufacturing settings. The achieved accuracy results, 0.9431 in classification and 0.9721 in prediction, are comparable to or better than the AI-based quality control results in other noninvasive methods of flat surface defect detection, and in the presented ultrasonic method, they are the first described in this way. This approach demonstrates the novelty and contribution of artificial intelligence (AI) methods and tools, significantly extending and automating existing applications of traditional methods. The susceptibility to augmentation by AI/ML may represent an important new property of traditional methods crucial to assessing their suitability for future Industry 4.0/5.0 applications.  
Industry 4.0/5.0; plastic sheets; airborne ultrasound; Lamb wave; identification of the presence of defects; artificial intelligence; AI; machine learning; ML