esa-sceva/satcom-chunk-collection huggingface dataset:
# Kirchhoff Migration for Identifying Unknown Targets Surrounded by Random Scatterers
## 4 Simulation Results
This scatterer's location was also identified by the \(\mathfrak{F}_{\mathrm{KIR}}(\mathbf{x})\) map, but its magnitude was small enough that the anomaly locations could still be accurately identified via Kirchhoff migration._  
**Example 3** (Selection of nonzero singular values: Cases 1 and 2). : _Figure 4 exhibits maps of \(\mathfrak{F}_{\mathrm{SUB}}(\mathbf{x})\) with various selection of nonzero singular values \(N_{0}\). By regarding the distribution of normalized singular values of \(\mathbb{K}\) in Figure 3d, the number \(N_{0}\) are chosen as \(3\), \(4\), and \(15\) for evaluating \(\mathfrak{F}_{\mathrm{SUB}}(\mathbf{x})\). Notice that if one selects \(N_{0}=3\), which is the same number of anomalies \(\mathbf{A}_{m}\), it is possible to obtain a good result. Furthermore, by comparing with the Figure 3e, this result is better than the one via the map of \(\mathfrak{F}_{\mathrm{KIR}}(\mathbf{x})\) so that subspace migration will provide better results to the Kirchhoff migration when appropriate number of singular values are chosen. In contrast, if \(N_{0}=4\) is chosen, Kirchhoff migration provides better results than subspace migration because the location of one random scatterer, located at \([-0.58,-0.42]^{\mathrm{T}}\), is retrieved clearly in the map of \(\mathfrak{F}_{\mathrm{SUB}}(\mathbf{x})\). If one selects \(N_{0}=15\), similar to the Figure 3e, the map of \(\mathfrak{F}_{\mathrm{SUB}}(\mathbf{x})\) provides locations of anomalies, single random scatterer, and unexpected artifacts. This result supports the discussions in Cases 1 and 2._  
**Example 4** (Case 3). : _In this Example, we consider the imaging results for Case 3. For this, we set the permittivities and radii of the random scatterers as \(\varepsilon_{\mathbf{R}}^{(s)}=\eta_{9}(4,5)\), \(\varepsilon_{\mathbf{R}}^{(s)}=\eta_{10}(4,5)\), and \(\sigma_{s}=\eta_{11}(0.07,0.14)\), respectively. In this case, there was no relationship between \(\alpha_{m}\) and \(\sigma_{s}\), \(\varepsilon_{A}^{(m)}\) and \(\varepsilon_{\mathbf{R}}^{(s)}\), or \(\mu_{\mathbf{A}}^{(m)}\) and \(\mu_{\mathbf{R}}^{(s)}\) for any \(m\) and \(s\), so it was impossible to distinguish locations of the anomalies and random scatterers accurately via subspace or Kirchhoff migration at either the low or high frequency, as shown in the Figure 5._  
Figure 2. (Example 1: Case 1) Distribution of normalized singular values, maps of \(\mathfrak{F}_{\mathrm{KIR}}(\mathbf{x})\) and \(\mathfrak{F}_{\mathrm{SUB}}(\mathbf{x})\) for \(\lambda=0.7\) (**top line**) and \(\lambda=0.2\) (**bottom line**). Figure 4: (Example 3: Case 2) Maps of \(\mathfrak{F}_{\rm SUB}(\mathbf{x})\) for \(\lambda=0.2\). Figure 3: (Example 2: Case 2) Distribution of normalized singular values, maps of \(\mathfrak{F}_{\rm KIR}(\mathbf{x})\) and \(\mathfrak{F}_{\rm SUB}(\mathbf{x})\) for \(\lambda=0.7\) (**top line**) and \(\lambda=0.2\) (**bottom line**).
## 1 Introduction
Li and Sun [32] presented a semi-analytical method to study the transverse vibration of an undamped elastically connected double-beam system with arbitrary boundary conditions. The transverse vibrations of a double-beam system interconnected by a viscoelastic layer were studied numerically [33]. The two beams can be restricted with different boundary conditions. Simsek and Cansz [34] studied the vibrations of elastically connected double-functionally graded beam systems with different boundary conditions under the action of a moving harmonic load by a numerical method. Zhang et al. [35] developed an analytical framework to study the transverse vibrations of double-beam systems made of two parallel Timoshenko beams connected by springs and coupled with various discontinuities. The free vibration, dynamic response and static buckling of an axially-loaded double-beam system with a viscoelastic layer were investigated by Chen and Sheu [36]. Li and Hua [37] introduced a spectral Finite Element method to study the vibrations of double-beam systems considering shear deformation and rotary inertia of the beams. The modal properties of the system were determined by applying the Muller root search algorithm. Palmeri and Adhikari [38] presented a Galerkin-type state-space approach for studying vibrations of a double-beam system interconnected by a viscoelastic layer. Various element matrixes for the double-beam systems were established, for example, in References [2; 3; 6; 39; 40]. In spite of the above studies about the forced vibrations of double-beam systems with various boundary conditions under the action of moving loads, to the author's best knowledge, only analytical solutions for the forced vibration of simply supported double-beam system are available in the literature. To understand the vibration mechanism of the system with various boundary conditions, the vibrations of double-beam system with five general kinds of symmetric boundary conditions (fixed-fixed, pinned-pinned, fixed-pinned, pinned-fixed and fixed-free) excited by a moving force are investigated by an analytical approach in this paper. The closed-form expressions to the modal properties of the double-beam system with the various symmetric boundary conditions are presented in Section 2. Parametric studies are also conducted to investigate the influences of the order of basic modes, contact stiffness ratio, mass ratio and beam stiffness ratio on the modal properties of the double-beam systems in this section. The analytical solutions to the vibrations of the double-beam system with the various boundary conditions subjected to a moving force are derived in Section 3. The effects of speed ratio, contact stiffness ratio, mass ratio and beam stiffness ratio on the dynamic responses of the system are also studied in a dimensionless form in this section. Conclusions are drawn in Section 4.
## 2 Summary of the Existing Test Results
### Meta1 Database of Prior Knowledge
Serial numbers 4 to 6 and 9 to 11 indicate the time and temperature of the soaking, subsequent drying, and subsequent heating which form the description of the wetting-drying cycles. Serial number 3 signifies the recurring cycle times of the same environmental attack, while the salt-attacked environment (serial numbers from 1 to 2) is obtained from technical specifications [50]. \[\varepsilon\ =\ \frac{\sigma}{E_{f}} \tag{1}\]  
\[\sigma\ =\ E_{f}\times\varepsilon \tag{2}\]  
\[\xi\ =\ \rho\times\delta \tag{3}\]  
\[P_{u}\ =\ \sigma\times S \tag{4}\]  
where \(\varepsilon\) is the elongation of the CFRP laminate, \(\sigma\) is the tensile strength of the CFRP laminate, \(E_{f}\) is the elasticity modulus of the CFRP laminate, \(\xi\) is the areal density of the CFRP laminate, \(\rho\) is the volume density of the CFRP laminate, \(\delta\) is the thickness of the CFRP laminate, \(P_{u}\) is the ultimate load, and \(S\) is the interfacial bond area. Due to the lack of a unified and comprehensive definition of failure modes across various literature sources, this model does not incorporate failure modes as input factors. Moreover, researches have demonstrated the superior shear and tensile strength of epoxy resin adhesive layers compared to concrete, with organic adhesives effectively permeating CFRP fibers [32; 35]. Consequently, the material properties of the epoxy resin adhesive are similarly omitted as factors. The data within Meta1 are segmented into 50 prediction tasks. Within each prediction task, samples are further subdivided into training and test sets in a 4:1 ratio. To ensure an adequate distribution of samples across tasks, an assumption of equal sample distribution is made, mitigating the risk of inadequate task samples. It is noteworthy that the Meta1 database integrates all data individually affected by hydrothermal and salt attacks. Therefore, for the former, the salt-related factors are set to 0, and for the latter, the hydrothermal-related data are set to 0. Consequently, for conventional neural networks, this dataset exhibits significant data gaps.
## 4 Materials and Methods
### Onto-Customisation Management System Architecture and Modular Workflow
** The driver selects their preferences via the DVI, specifying which settings they want to adjust. * **Phase 2: DVI queries the Onto-CMS to check modifiability. ** The DVI then queries the Onto-CMS to determine which preferences can be modified. It communicates with the driver profile ontology to check the modifiability of the requested preferences. The ontology defines the structure and rules governing driver preferences (e.g., which settings can be modified based on the driver's profile) and sets any restrictions (for example, novice drivers may have limited ability to modify ADAS settings). * **Phase 3: KG real-time data check. ** At the same time, the Onto-CMS queries the KG, which stores real-time data regarding the driver, such as their driving experience, preferences and restrictions. The Onto-CMS and KG work together to evaluate if requested changes are feasible and comply with the driver's unique profile. Figure 2: Workflow of Onto-CMS. * **Phase 4: Modifiability: Allowed or not. ** Once both the ontology and the KG confirm that specific preferences can be modified, the system proceeds. If YES, the changes are permitted. The DVI sends the updates to the CAN-BUS, ensuring the KG is updated synchronously with all modifications. The CAN-BUS then transmits these updates to the Domain Gateway, which routes them to the appropriate ECUs (Propulsion, Safety, Comfort and ADAS). If NO, the requested modification is denied. The DVI knows that some preferences cannot be modified by the Onto-CMS, and it notifies the driver that this modification is not possible. This allows the driver to always know when changes are rejected by the modifiability tests performed by the ontology and the KG. In this case, the DVI does not send any messages to the ECUs, only a notification to the driver. * **Phase 5: ECUs update preferences. ** After the ECUs update their settings based on the driver's preferences, it sends feedback to the DVI. This feedback includes confirmation of the successful update or details about any errors that occurred. The system only sends changes to the ECUs if the modifiability check is authorised, ensuring that unauthorised or potentially dangerous modifications do not take place. * **Phase 6: Feedback to the driver. ** Once the ECUs implement the changes to the driver's preferences, the Onto-CMS performs the following functions: (i) it updates the KG to record the latest preferences, ensuring the KG reflects the current state of the system, and (ii) it sends feedback to the driver to notify them that their preferences have been updated.
# End-to-End Online Video Stitching and Stabilization Method Based on Unsupervised Deep Learning
## 3 Experimental Results and Analysis
### Visual Effect Comparison  
To visually demonstrate the advantages of the proposed method in video stitching and stability, we selected representative cases and compared them with the popular StabStitch method. Since StabStitch often sacrifices the accuracy of early-stage stitching for smoothness in the path estimation phase, it is difficult to visually reflect its flaws in PSNR/SSIM metrics. Therefore, we specifically visualized part of the stitching results, as shown in Figures 4 and 5.  
In the comparison images, it is evident that in the overlapping region between the reference and target frames, the stitched image generated using StabStitch contains noticeable artifacts, which are highlighted by red boxes. The generation of these artifacts can mainly be attributed to the large compensation required in the target frame for a smooth transition during fast camera movements, while the reference frame fails to synchronize with effective compensation. This results in a clear misalignment in the overlapping area. In contrast, our method applies motion compensation between both the target and reference frames and uses L1 photometric loss in the overlapping region to align the image content. This not only ensures video stability but also significantly improves the alignment accuracy of the overlapping region, resulting in a more natural and coherent visual effect.
# COVID-19 Detection Model with Acoustic Features from Cough Sound and Its Application
## 6 Conclusions  
In this study, we proposed a COVID-19 diagnostic model and its application based on an artificial intelligence (AI) model with optimized feature vectors using cough sounds. The Bhattacharyya distance was used to measure the separability of features from COVID-19-positive cough data and negative cough data. MFCC, \(\Delta\)-MFCC, \(\Delta^{2}\)-MFCC, spectral contrast, chroma, spectral flatness, spectral bandwidth, spectral roll-off, RMS energy, spectral centroid, zero-crossing rate, and onset showed high values, in that order. The highest-valued MFCC had a value of 0.207171. Subsequently, \(\Delta^{2}\)-MFCC had a value of 0.149195, \(\Delta\)-MFCC had a value of 0.099828, and spectral contrast had a value of 0.090616. These top four features made up the feature set that this study proposed. After training the combined ResNet-50 and DNN model, the result had an accuracy of 0.96, a sensitivity of 0.95, a specificity of 0.96, and a precision of 0.95. Using this model, an application for Android was developed so that many people could use it for COVID-19 testing. The COVID-19 test model using cough sounds, the result of this study, has a simpler procedure and lower cost than the polymerase chain reaction (PCR) test that analyzes genes. Moreover, it is expected that this application will be a useful tool for those who are unable to do a PCR test, as it is difficult to insert a cotton swab into the nasopharynx due to anatomical or medical issues. In future studies, the model can be upgraded by using not only cough sound data but also clinical information data, including information on fever, headache, and other symptoms. In addition, if more quality cough sound data are collected and utilised, improved results can be expected.  
Conceptualization, S.-P.L.; methodology, S.K. and J.-Y.B.; investigation, S.K. and J.-Y.B.; writing--original draft preparation, S.K.; writing--review and editing, S.-P.L.; project administration, S.-P.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported by Sangmyung University.  
Not applicable.  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
Data Availability Statement: Experiments used publicly available datasets.  
Figure 7: Application screen 2: (**a**) positive result; (**b**) negative result; (**c**) if no cough is detected.
## 4 Results
### Production
#### 4.2.3 Feeds  
Two types of feeds were used by the chicken layer farms in our study. The first was a concentrated feed supplied by an animal feed business. Concentrated feeds were mixed with feed blends supplied by a cooperative mill, and the ratios between feeds varied between individual farms.  
Figure 1: Production cycle of chicken layer farm.  
The second was a mixed feed. The raw materials used to produce chicken feed included maize, soybean meal, crushed acacia, fish meal, crushed shellfish, and di-calcium. In 1999, chicken layer farmers in Ban Klang formed a cooperative to manage and provide raw feed materials. The cooperative mill produces semi-completed feeds to sell to its members. At the time of our study, all the materials were supplied by the cooperative.  
Considering the size of the farms, the labor demand was relatively small, with many of the farms employing family laborers. Some farms hired additional laborers for daily feeding for 1-2 h per day. Machines were also part of the feeding process, including a feed-mixer. Feeding, cleaning, and egg collection were performed by household laborers. A daily schedule for chicken layer farms in Ban Klang is as follows:  
1. Feed is prepared between 0500 and 0600 h. Chicken barns are cleaned while the chicken feed is mixed. Between 0600 and 0700 h, laborers feed the hens using feed trays. Between 0800 and 0900 h, eggs are collected.
2. Between 0900 and 1600 h, eggs are cleaned, sorted by size, and packaged. Family laborers repair the barn, vaccinate the animals, and perform other daily activities.
3. Between 1600 and 1700 h, the chickens are fed, and eggs are once again collected.  
According to a chicken layer farmer that we interviewed:  
\"Chicken layer farming requires full-time working all day. I cannot stop working because chickens need to be fed every day. Even when I need medication, I have to go to the private clinic because going to the public hospital is time-consuming. Raising chickens is full-time, strenuous works\", according to field notes of the interview with [PERSON].  
The strenuous labor of cultivating rice or vegetables is primarily during the growing and harvesting periods. In contrast, chicken layer farming requires daily labor all year, typically performed by household laborers.
## 4 Wettability
### The Effect of Surface Roughness on Wettability
Most findings indicate that surface roughness improves the hydrophobicity of the hydrophobic surfaces and the hydrophilicity of the hydrophilic surfaces, whereas some scholars show controversial results. Most researchers have focused on proving whether surface roughness is beneficial or detrimental to the wettability of mineral surfaces by comparing contact angles across a narrow range of surface roughness values. Consequently, few investigations have examined how wettability varies across a wide range of surface roughness values, which may lead to unreliable conclusions. Additionally, while hydrophilic minerals are often selected to study the relationship between wettability and surface roughness, there is a lack of studies on the surfaces of natural hydrophobic minerals. Furthermore, most studies focus on the relationship between surface roughness and the wettability of a single mineral, with limited studies on how varying the surface roughness of different minerals can enhance the differences in wettability and facilitate selective flotation separation.
# Chaotic Image Encryption: State-of-the-Art, Ecosystem, and Future Roadmap
## 5 Future Roadmap: The Promise of AI
### Neural-like Image Encryption  
[PERSON] et al. [253] proposed an image encryption algorithm using a neuron-like scheme. The image information is used to adjust neuron weightings to achieve effective pixel diffusion. Simulation results demonstrated strong cryptographic properties.  
Combinatorial Optimization Problems  
Many important problems in computing such as the shortest path, the Traveling Salesman Problem (TSP), Cellular Channel Assignment (CCA), or more generally, sequencing and resource allocation problems have very inefficient exhaustive search solutions [254]. To find the optimal solution, neural networks have been utilized. Some examples are given below.  
In a proposal by [PERSON] and [PERSON] [255], a CNN was used to optimize the shortest path problem. In this proposal, a novel post-processing technique was utilized to achieve the optimal solution with high probability. In a different proposal, [PERSON] et al. [256] used a CNN with the novel ability to characterize local features to solve the TSP with a higher-than-average success rate for obtaining the optimal solution.  
Work on optimizing CCA was conducted by [PERSON] et al. [257] and [PERSON] and [PERSON] [258], where noisy CNNs and transient CNNs were utilized, respectively. A related problem to CCA is the more general broadcast scheduling problem. Work by [PERSON] et al. [259, 260] proposed a novel noise-tuning-based Hysteretic Noisy Chaotic Neural Network (NHNCNN) that optimizes the problem by obtaining the minimum frame length and maximal channel usage. Their work was completed for both wireless multihop networks and pocket radio networks.  
The same CNNs can also be used to solve multiple types of optimization problems. For instance, [PERSON] et al. [261] combined the best parts of the existing Chaotic Simulated Annealing (CSA) and Stochastic Simulated Annealing (SSA) techniques to optimize multiple combinatorial problems such as the TSP and CCA. To compare and contrast the many different CNN approaches for solving these problems, [PERSON] and [PERSON] [262] developed a framework.
## 6 Comparison with Theoretical Approaches
### Theoretical Models vs. ML Models  
When comparing the prediction performance of RCSW shear capacity between traditional theoretical models and ML models, significant differences can be seen. First, in Table 8, for the performance metrics of the four theoretical models, the Hwang and Lee model shows the best prediction performance with an RMSE of 291.1, an MSE of 68,166.2, an MAE of 172.1, and an R\({}^{2}\) of 0.87, which indicates a good ability to explain the variability of the data. Among the machine learning models, DT has an RMSE of 279.4 on the test set, which is significantly better than most theoretical models. RF also performs well on the test set with an RMSE of 285.4 and an R\({}^{2}\) that can reach 0.93, indicating high prediction accuracy and the ability to account for data variability. Gradient Boosting and DNN models also show good performance, achieving an RMSE of 66.2 and 85.4 on the test set, respectively.  
Overall, ML models generally show better predictive performance. ML methods, such as RF and Gradient Boosting, are able to achieve a better balance of accuracy and interpretability than traditional models. In summary, ML models may be more advantageous in the forecasting of the shear strength of RCSWs, especially with high data diversity and complexity. It can be seen that the shear mechanism of RCDW is a complex process. The traditional theoretical models are often limited by multiple assumptions, and the influence of the parameters considered is relatively limited, so they are often difficult to accurately and reliably reflect the complex nonlinear relationship between the parameters and the shear capacity of RCSWs. In contrast, the ML models, with powerful nonlinear processing  
Figure 9: Comparison of predicted load carrying capacity of theoretical model and test load carrying capacity.  
capabilities, high-dimensional data processing capabilities, autonomous feature selection capabilities, strong ensemble learning capabilities, good adaptability, and high robustness, are able to deeply capture the mapping relationship between input features and the shear strength of RCSWs.