esa-sceva/satcom-chunk-collection huggingface dataset:
# A Comparative Analysis of Feature Detectors and Descriptors for Image Stitching
## 2 Overview of Feature Detectors and Descriptors
### Orb  
The authors of [54] developed an Oriented FAST and Rotated BRIEF (ORB) detector and descriptor as an efficient alternative to SIFT and SURF. ORB is a combination of the FAST detector and the BRIEF descriptor, with several enhancements to improve the performance. ORB adds a fast and accurate orientation component to the FAST algorithm. It uses an image pyramid to generate multiscale features. For the descriptor, it steers BRIEF according to the orientation of key-points to make it rotation-invariant.
## 6 Chain Signature
### Chain Signature Process  
Algorithm 3 shows the process of chain signature for both parties in communication. The messages transmitted by the two communicating parties include messages with a signature and those without a signature, and the chain signature interval is \(d\). In the process, the communication node constructs the message hash chain and transmits the messages synchronously, e.g., the node encapsulates the \(m_{\delta}\) and \(HC_{\delta}\) constructed according to the \(m_{\delta}\) into a message \(p_{\delta}\) and sends it to the destination. For the security of the message hash chain, the sender will chain-sign the message when the signature counter reaches \(d\) or when necessary, e.g., after the sender signs \(HC_{\delta}\), it only needs to sign \(HC_{\delta+d}\) next time. The structure of a message with a signature is \(p_{i}=(m_{i},seq_{i},HC_{i},s_{i})\), and a message without a signature is \(p_{j}=(m_{j},seq_{j},HC_{j})\). The process of the sender encapsulating the messages shown in Algorithm 3. The parameters used in Algorithm 3 are described below:  
* \(\mathit{cur\_interval}\): Current signature interval.
* \(\mathit{Sig\_interval}\): A signature is required when the sender's signature interval reaches \(\mathit{Sig\_interval}\).
* \(\mathit{EnPkt}\): The function that encapsulates parameters as header of message hash chain.
* \(\mathit{Sig}\): The signature function described in Section 7.
* \(\mathit{eks_{src}}\): Sender's private key.  
**Input**: Header content, signature interval, payload.  
**Output**: Encapsulated MHC datagram.  
```
1:According to the content of the message, the payload and the tail node of the message hash chain, a node value \(HC\_node\gets HC\_Iteration(hdr,payload,HC\_node)\) of the chain is generated.
2:The sender inserts \(HC\_node\) at the end of the message hash chain.
3:The sender updates message hash chain tail node \(HC\_node=HC\_node\).
4:if\(\mathit{cur\_interval}<\mathit{Sig\_interval}\)then
5:The sender encapsulates the header \(p\gets EnPkt(hdr,payload,HC\_node)\).
6:else
7:The sender computes the signature \(s\gets Sig(ek_{src},HC\_node)\).
8:\(p\gets EnPkt(hdr,payload,HC\_node,s)\)
9:endif
10:return\(p\)
```  
**Algorithm 3** Message Hash Chain Encapsulates Messages Header
## 4 Materials
### Trajectory Algorithm  
The DAA produces displacement steps, thus the displacement evolution in space is well represented by a discrete state-space, or a grid. Therefore, grid-based pathfinding algorithms are a good choice for our conveyance task. Consequently, an A* trajectory algorithm, based on the presented dynamic model, was developed. Its objective is to transport the object to any succession of target points, avoiding the possible obstacles and maximizing the performance of the system. To solve the conveyance trajectory, we suppose to know the initial position of the object's center, the list of target points and the obstacles coordinates. First, we divide the conveyance plane into a grid representing the possible positions and transitions of the object on the DAA. The target positions and obstacles are integrated into the grid, and then the grid is injected into the trajectory algorithm to find the succession of displacement steps that link all target points. The distance and direction of each step is then sent to the dynamic model, that defines the control currents that produce the desired displacement steps, minimizing the current intensities. Finally, these control currents are sent to a virtual system as well as to the real system simultaneously to visualize and evaluate the generated displacements (Figure 8).  
Our previous work demonstrated that an optimum control strategy in terms of energy and final position error was to use the maximum axial displacement at each step to approach the target point and then use a correction step at the end of the trajectory to attain the final position [28]. The A* algorithm uses this control strategy, generating a grid that allows displacements only in the axial directions. Each node of the grid is at the maximum displacement step distance from each other. The target points are approximated  
Figure 8: Interface: A pathfinding problem with 3 target points and two obstacles solved with the adapted A* algorithm and dynamic model (and a 1 mm safety distance to the obstacles).  
Figure 7: Representation of the current signals used during experimentation.  
to the nearest node and, once the object arrives there, the difference to the real target is then injected into the inverse model to find the correction step. As the A* algorithm minimizes the distance to the obstacles in its search for the optimum trajectory, we included an additional parameter, called safety margin, to force the path to respect a distance to the obstacles. This margin is set to 1 mm in the example of Figure 8.
# An Empirical Study of the Code Generation of Safety-Critical Software Using LLMs
## 4 Experiments
### Code Generation Based on Software Requirements through Augmented Prompt
#### 4.4.5 Performance Requirements for the Code  
In safety-critical domains, real-time performance is a key metric for software, and thus, there is a high demand for software performance. However, since LLMs, as a large language model, cannot execute programs on their own, they cannot directly judge whether the generated code meets the time performance requirements. As a workaround, we can use performance prompts to indicate LLMs to optimize the program's performance. If the tests show that the generated code does not meet the performance requirements, we can request LLMs to perform performance analysis on the code, identify performance bottlenecks, and then optimize the performance. This process involves iterations. For example in Box 11:  
```
RNI_NFUNR_07: Performance requirements: execution time less than 100 us.
```  
Based on the aforementioned augmented prompting method, the NucRate algorithm and cruise control software code are re-generated (the entire code is in Appendix A Listing A3 and Appendix B Listing A6).  
It is worth noting that even if we provide LLMs with all the necessary requirements, they may still miss some requirements during code generation (such as the MISRA specification mentioned earlier). In such cases, the non-implemented requirements will be reinforced by appending additional prompts. For example, if the code comment rate is insufficient, or the code does not comply with the MISRA specification, these specific requirements will be emphasized, and LLMs will generate code that fully satisfies the requirements. So, the iterations in LLMs are usually needed.  
Robust testing is about improving reliability and finding those corner cases by inputting data that mimic extreme environmental conditions to help determine whether or not the system is robust enough to deliver [40]. In order to ensure its reliability and safety of the generated code, we conducted the robustness testing of the generated code. Test cases and results are shown in Table 2. The test code is in Appendix C Listing A7.  
The code generated through the aforementioned augmented prompts has been manually reviewed, analyzed, and tested to ensure that it meets the corresponding functional requirements and industry standards. It can be integrated and applied in practical engineering.
###### Abstract  
This study presents an enhanced version of the StarGAN model, with a focus on medical applications, particularly endoscopic image augmentation. Our model incorporates novel Perspective Transformation and Viewpoint Attention Modules for StarGAN that improve image classification accuracy in a multiclass classification task. The Perspective Transformation Module enables the generation of more diverse viewing angles, while the Viewpoint Attention Module helps focus on diagnostically significant regions. We evaluate the performance of our enhanced architecture using the Kvasir v2 dataset, which contains 8000 images across eight gastrointestinal disease classes, comparing it against baseline models including VGG-16, ResNet-50, DenseNet-121, InceptionNet-V3, and EfficientNet-B7. Experimental results demonstrate that our approach achieves better performance in all models for this eight-class classification problem, increasing accuracy on average by 0.7% on VGG-16 and 0.63% on EfficientNet-B7 models. The addition of perspective transformation capabilities enables more diverse examples to augment the database and provide more samples of specific illnesses. Our approach offers a promising solution for medical image generation, enabling effective training with fewer data samples, which is particularly valuable in medical model development where data are often scarce due to challenges in acquisition. These improvements demonstrate significant potential for advancing machine learning disease classification systems in gastroenterology and medical image augmentation as a whole.  
Keywords:data augmentation; generative adversarial networks; medical diagnostic imaging; image recognition +
Footnote †: journal: Applied Sciences
# Non-Newtonian Behaviors of Crumb Rubber-Modified Bituminous Binders
## 2 Materials and Methods
### Binder Preparation  
The CRMB was produced in the laboratory by blending 18% CRMs by the weight of the total blend based on the previous studies [23] and trial mixing. Adding more than 18% CRMs will cause mixing problems and undesired binder properties (e.g., extremely high viscosity). Manual stirring for 5 min was applied to pre-distribute CRMs into the base bitumen, then the blend was mixed using a Silverson high shear mixer with a square hole screen (Figure 2) at 180 \({}^{\circ}\)C with the shearing speed of 6000 rpm for 30 min. This mixing condition was optimized based on the criteria to obtain better mechanical properties of CRMB. During the bitumen-rubber interaction, there are generally two processes happens: rubber swelling and chemical degradation [24]. Mixing at relatively low temperatures causes inefficient swelling of rubber, while mixing at relatively high temperatures results in severe degradation of rubber particles. Both scenarios are detrimental to the property development of CRMB. During the laboratory mixing process, the mixing head was immersed into the hot bitumen to avoid vortex which may involve the potential oxygen. Each warm mix additive was incorporated into both base bitumen and CRMB with the same dosage at 140 \({}^{\circ}\)C. The percentages of additives W and C were 2.0% and 0.6% based on manufacturers' recommended dosage and preliminary tests. Therefore, a total of six types bituminous binders were prepared in this study, namely 70/100, 70/100-W, 70/100-C, CRMB, CRMB-W, and CRMB-C. To solely investigate the influence of warm mix additives on the rheological properties of binders, all binder samples were tested in fresh states without artificial ageing.  
Figure 1: Environment scanning electron microscopy (ESEM) images of (**a**) CRM and (**b**) base bitumen.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Properties** & **Description or Value** \\ \hline Source & Scrap truck tyres \\ Colour & Black \\ Morphology & Porous \\ Specific gravity (kg/L) & 1.15 \\ Decomposition temperature (\({}^{\circ}\)C) & \(\sim\)200 \\ Total rubber (natural and synthetic) & 55 \\ Carbon Black (\%) & 30 \\ Zinc oxide (\%) & 1.5 \\ Sulphur (\%) & 1 \\ Benzene extraction (\%) & 5.5 \\ Ash content (\%) & 7 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Basic properties of CRM.
# Effect of Strain Hardening on Increase in Collapse Pressure during the Manufacture of ERW Pipe
## 3 Evaluating Collapse Pressure
### Effect of the Sizing Process  
The effect of the sizing ratio on the collapse pressure of pipes with various sizes was compared, as shown Figure 10. The collapse pressure was normalized by the nominal yield strength and the diameter-to-thickness ratio (_D_/_t_). The maximum sizing ratio was approximately 1.0%, and the _D_/_t_ ratio ranged from 14 to 36, which is commonly used in practice. The results clearly show that the collapse pressure was greater for a smaller _D_/_t_. Moreover, the correlation between the sizing ratio and the collapse pressure was more distinct for a smaller _D_/_t_. When the _D_/_t_ was 14, the collapse pressure increased with an increase in the sizing ratio. An approximate 10 to 15% increase in the collapse pressure could be achieved with sizing ratios greater than 0.4%, because inelastic local buckling occurs for a smaller _D_/_t_ so that the collapse pressure could be additionally affected by the plastic behavior of the material, as well as by the improved out-of-roundness of the cross-section. [PERSON] et al. [3] confirmed an improvement in out-of-roundness due to a larger sizing ratio. For a _D_/_t_ greater than 22, there was no clear correlation between the sizing ratio and the collapse pressure. Dispersion of the collapse pressure became smaller with a greater _D_/_t_, because elastic local buckling was dominant for a larger _D_/_t_. Since improvement in the collapse pressure due to a larger sizing ratio was unclear, either the sizing process is unnecessary or only a smaller sizing ratio is needed for a _D_/_t_ greater than 22.  
Figure 9: Collapse analysis: (**a**) boundary and load conditions; (**b**) displacement under external pressure.  
As shown in Figure 11, the maximum strains that initiated collapse for various \(D/t\) ratios are marked on the stress-strain curves in the circumferential direction. The sizing ratios of all cases were approximately 1.0%. The results clearly show that the maximum strain at the point of collapse was greater for a smaller \(D/t\). This observation indicates that a smaller \(D/t\) could withstand more strain energy because the collapse pressure is greater. When \(D/t\) is 31 or greater, the maximum strains were located before the yield plateau due to elastic buckling. Thus, the influence of the sizing was minor.  
Figure 10: Effect of the sizing ratio on the collapse pressure: (**a**) \(D/t=14\); (**b**) \(D/t=19\); (**c**) \(D/t=22\); (**d**) \(D/t=26\) and 28; (**e**) \(D/t=31\) and 33; (**f**) \(D/t=36\).
# Relation of EDL Forces between Clay Particles Calculated by Different Methods
## 2 Analysis on the Previous Calculation Methods of EDL Force between Clay Particles  
### Previous Methods  
As shown in Figure 1, the total EDL force felt by charged clay particles in the electrolyte solution results from two parts: osmotic repulsive stress and electrical (Maxwell) attractive stress.  
The total EDL force can be calculated by [33]:  
\[r_{\rm i}^{\rm total}=-\oint\!\!\!\!\!\int_{\Gamma}\cosh(\phi)n_{\rm i}{\rm d} \Gamma+\oint\!\!\!\!\!\int_{\Gamma}\left(\phi_{\rm i}\phi_{\rm i}n_{\rm j}- \frac{1}{2}\phi_{\rm,k}\phi_{\rm,k}n_{\rm i}\right){\rm d}\Gamma\quad{\rm i},{ \rm j},{\rm k}={\rm x},{\rm y},{\rm z} \tag{1}\]  
where \(\phi\) is the dimensionless potential, \(\Gamma\) is an arbitrary boundary enclosing a simply connected domain within which the clay particle exists, and \(n_{\rm j}\) is the direction cosine of the outward normal of this boundary. On the right hand side of the above equation, the item in the first parentheses represents the contribution of osmotic repulsive stresses, while that in the second parentheses denotes the contribution of Maxwell attractive stresses. It has been shown that the calculated total EDL force is independent of the position and size of the enclosed boundary \(\Gamma\) as long as the boundary encloses either one of the two interacting charged clay particle [23; 31; 33].  
In fact, the first integration on the RHS of Equation (1) can also be calculated by integrating the electrostatic force density over the domain \(\Omega\) that the above boundary \(\Gamma\) is enclosing as [29; 32]:  
\[R_{\rm i}=-\oint\!\!\!\!\!\int_{\Gamma}{\rm cosh}(\phi)n_{\rm i}{\rm d}\Gamma \tag{2}\]  
\[=-\int\!\!\!\!\!\int\!\!\!\!\!\int_{\Omega}\left({\rm cosh}(\phi)\right)_{,{ \rm i}}{\rm d}\Omega \tag{3}\]  
Similarly, using the well-known divergence theorem, the second integration on the RHS of Equation (1) can be rewritten as:  
\[\overline{R}_{\rm j}=\oint\!\!\!\!\!\int_{\Gamma}\left(\phi_{\rm j}\phi_{\rm i }n_{\rm j}-\frac{1}{2}\phi_{\rm,k}\phi_{\rm,k}n_{\rm i}\right){\rm d}\Gamma \tag{4}\]  
\[=\int\!\!\!\!\!\int\!\!\!\!\int_{\Omega}\phi_{\rm,i}\phi_{\rm,j}{\rm d}\Omega \tag{5}\]  
in which the integrand is the dimensionless gradient of the Maxwell stress tensor. As this gradient is the same as the electrostatic force density [29], it can be easily shown that \(-R_{\rm i}\) is the same as \(\overline{R}_{\rm j}\) as long as they have the same integral domain \(\Omega\).
# Advances in Three Dimensional Bioprinting for Wound Healing: A Comprehensive Review
## 6 Adaptability of 3D Bioprinting to Industry
### Lifespan and Durability  
Three-dimensional-bioprinted dressings are highly versatile and can be customized to suit wound needs. Nevertheless, traditional dressings are often composed and structured in a manner that may not always correspond exactly to the wound's requirements. Using bioprinting, the dressing's composition, structure and mechanical properties can be precisely controlled. Due to the technologies' flexibility in using different types of materials individually and together as bioink, dressings can be designed that are tailored to specific wound types and stages of healing. An additional important function of dressings is to adapt to the dynamic environment of a healing wound through their mechanical and physical durability. Creating an effective wound dressing requires the synchronization of the degradation of the dressing with the growth of new tissue. For instance, the hydrogels specifically chosen for application in wound healing models must maintain their structure for a minimum of 14 days before being applied in vivo [289]. Along with the degradation, the mechanical forces generated by daily body movements can disrupt the integrity of the hydrogel mesh structure, which can reduce the therapeutic effect of the hydrogel. Despite this, hydrogel materials with self-healing abilities can maintain structural and functional integrity in a different manner than traditional dressings. When the network structure is disrupted, self-healing hydrogels can extend the lifespan of bioprinting products and improve safety compared to conventional wound dressings [289; 290; 291].
# How Useful Are Strain Rates for Estimating the Long-Term Spatial Distribution of Earthquakes?
## 2 Method and Data
### Molchan Error Diagram  
We obtain Molchan error diagrams [19; 20] for all covariates at different time intervals. Molchan error diagrams are equivalent to Receiver Operating Characteristic curves, which have been widely used in other contexts [21; 22]. They can be used to investigate the correlation between a spatially distributed covariate and the spatial distribution of target earthquakes.  
The error diagram is based on the concept of an alarm being declared wherever a covariate exceeds a certain threshold. As the threshold is decreased, the proportion of space occupied by alarms increases, and the proportion of target earthquakes outside of the alarm space (i.e., the proportion of \"unpredicted earthquakes\") decreases. In the error diagram, the proportion of unpredicted earthquakes is plotted against the proportion of  
Figure 1: Spatial distributions of the modified (**a**) Proximity to plate interface (PPI) and (**b**) Proximity to mapped faults (PMF) covariates (log of grid cell values). The colour scale shows the logarithm of the relative rate. (**c**) Spatial distribution of updated Fault in cell (FLT) binary covariate using all faults from the New Zealand Community Fault Model [18].  
Figure 2: Spatial distribution of (**a**) GSS (1996–2013), (**b**) BSS (1996–2011), and (**c**) HWS (1995–2013) strain rate models in the New Zealand CSEP testing region. The colour scale shows the logarithm of the relative rate.  
space occupied by alarms, but the threshold for declaring an alarm is not shown. The error diagram is, therefore, a monotone decreasing curve.  
For an ideal predictor, all target earthquakes are predicted by an alarm occupying almost zero area. The error diagram curve then consists of the line segments joining the point (0,1) to (0,0) and the point (0,0) to (1,0). For a totally uninformative predictor, the proportion of space occupied would be the same as the proportion of predicted earthquakes. The error diagram curve would then be the diagonal line joining (1,0) to (0,1).  
The area above the error diagram curve within the unit box is called the Area Skill Score (ASS) [23]. The ASS is a rough measure of the overall potential the covariate has for predicting the locations of target earthquakes. An ideal predictor has an ASS of 1, and a totally uninformative predictor has an ASS of 0.5.  
If a covariate is better correlated with the locations of target earthquakes during one time period than another, then the error diagram will vary with time accordingly. Therefore, we plot and compare error diagrams for different periods.
## 3 Micro-Accelerator-in-the-Loop Framework
### MA RTL Generation and Cycle-Accurate Simulation
There are clock cycle delays, depending on the column index of weight. The MAC result for each PE is the output when all inputs and weights were consumed at the PEs. The MA generated for the CNN model, the PE matrix is generated by the product of the output column and row made by the filter and the filter column. A CNN MA sums each PE in a row into one output value. Similar to FC MA, a delay written to the register is used for weights and inputs flowing into the PE matrix. We employed a conventional systolic accelerator structure, rather than state-of-the-art designs, for the RTL generation process implemented by MAIL. Because the scope of this paper is not the accelerator design but rather the introduction of a framework that enables performance evaluation by linking MA and TinyML applications. The internal PE structure of the systolic array generated as MA's RTL is illustrated in Figure 4. Considering the characteristics of TinyML, we assumed a typical systolic array rather than a custom accelerator architecture dedicated to specific applications. Therefore, MA utilizes PEs that employ a weight-stationary dataflow. The weights used for MAC operations in each PE are preloaded from a buffer. The input data, including activations,  
Figure 3: MAIL framework MA RTL generation scheme: FC and CNN layer. Generated MA RTL pass through cycle-accurate RTL evaluation and feedback profile result to system emulator. flow from top to bottom, simultaneously being used for multiplication with the weights and passing through to the next PE in the downward direction. The partial sum required to generate the output flows from left to right.