esa-sceva/satcom-chunk-collection huggingface dataset:
# From the Quantum Approximate Optimization Algorithm to a Quantum Alternating Operator Ansatz
## 4 QAOA Mappings: Strings
### Example: MinGraphColoring  
**Problem.** Given a graph \(G=(V,E)\), find the minimal number of colors \(k^{*}\) required to properly color it.  
A graph that can be \(\kappa\)-colored but not \((\kappa-1)\)-colored is said to have _chromatic number_\(\kappa\). We took as our configuration space the set of \(\kappa\)-dit strings of length \(n\), where \(\kappa=D_{G}+2\). The domain \(F\) is the set of _proper_ colorings, many of which will use fewer than \(\kappa\) colors. With \(D_{G}+2\) colors, as we explain next, it is possible to get from any proper coloring to any other by local moves while staying in thefeasible subspace, a property we made use of in designing mixing operators. We comment that it may be advantageous to take use a larger number of colors since that may promote mixing, but the tradeoffs there would need to be determined in a future investigation.  
It is easy to see that any graph can be colored with \(D_{G}+1\) colors. To see that \(\kappa=D_{G}+2\) suffices to get between any two \(D_{G}+1\) colorings, first recognize that given a \(D_{G}+2\) coloring, one can always obtain a \(D_{G}+1\) coloring by simply choosing a color and recoloring each vertex currently colored in that color with one of the other colors, since at least one of those colors will not be used by its neighbors. This move is local, in that it depends only on the neighborhood of the vertex. Now, given two \(D_{G}+1\) colorings \(C\) and \(C^{\prime}\), we iterated through colors \(c\) to tranform between the two colorings via local moves while staying in the feasible space. Let \(S^{\prime}\subset V\) be the set of vertices colored \(c\) in \(C^{\prime}\), and let \(S\subset S^{\prime}\) be the set of vertices in \(S^{\prime}\) that are not colored \(c\) in \(C\). Consider all neighbors of vertices in \(S\). For any neighbor colored \(c\), color it with the unused color. We are now free to color all vertices in \(S\) with color \(c\). Iterating through the \(\kappa\) colors provides a means of getting from one \(D_{G}+1\) coloring to another by local moves that remain in the feasible space.
## 3 Obstacle Avoidance Scenario
### Obstacle Avoidance Techniques  
As presented in the previous sections, the flying complexity of a sprayer UAV is quite different from a general UAV. Besides that, the obstacles are static and separated on farm-lands, as described in Section 2.3. This is why the obstacle avoidance techniques need to be different from others. Although there are some research papers on obstacle avoidance and path planning using satellite images (Section 2.1), to the best of our knowledge, no research work specifically on local obstacle avoidance for sprayer UAVs has been conducted.  
Using local satellite images for obstacle avoidance and path planning on farmlands may be deleterious. Because the satellite data updates after a particular schedule, sometimes the images get updated before an obstacle appears, which may cause accidents. Moreover, after updating the image of an obstacle, the obstacle may disappear, which may cause unwanted path generation. Sometimes even a small or narrow obstacle cannot be seen from the satellite image. Thus, avoiding obstacles locally is very important for sprayer UAVs. Several works have been done on local obstacle avoidance for mobile robots as well as UAVs. The subsequent subsections will present some of these obstacle avoidance approaches from different studies.
# Water Jacket Systems for Temperature Control of Petri Dish Cell Culture Chambers
## 2 Materials and Methods
### Computational Fluid Dynamics (CFD) Simulations  
A comprehensive set of CFD simulations were conducted to analyze the thermal performance of the water jacket system under various geometric, operational, and environmental conditions. Simulations were conducted in three dimensions under both steady state (stable) and unsteady state (transient) conditions, using ANSYS Fluent software.  
The differential equations governing mass balance, momentum, and energy for the water stored inside the water jacket chamber (as given in Equations (6)-(8)) are solved. For the other components of the system, including the water jacket structure, the sample stored inside the Petri dish, the Petri dish structure, and the washer surrounding the Petri dish, only the energy balance equation (as given in Equations (9)-(12)) is solved, as there is no flow within these components.  
Water recirculating through the water chamber:  
\[\
abla\cdot\overset{\rightarrow}{U}=0 \tag{6}\]  
\[\rho_{water}\left(\overset{\rightarrow}{U}\cdot\
abla\right)\overset{ \rightarrow}{U}=-\
abla P+\mu_{water}\;\
abla^{2}\overset{\rightarrow}{U} \tag{7}\]  
\[\left(\rho c_{p}\right)_{water}\left(\frac{\partial T}{\partial t}+\left( \overset{\rightarrow}{U}\cdot\
abla\right)\,T\right)=k_{water}\;\
abla^{2}T \tag{8}\]  
Water jacket walls:  
\[\left(\rho c_{p}\right)_{jacket}=k_{jacket}\
abla^{2}T \tag{9}\]  
Sample inside the Petri dish:  
\[\left(\rho c_{p}\right)_{sample}=k_{sample}\;\
abla^{2}T \tag{10}\]  
Petri dish walls:  
\[\left(\rho c_{p}\right)_{Petri\;dish}=k_{Petri\;dish}\;\
abla^{2}T \tag{11}\]  
Rubber washer:  
\[\left(\rho c_{p}\right)_{washr}=k_{washer}\;\
abla^{2}T \tag{12}\]  
where \(U\) and \(P\) are the velocity and pressure of the liquid, \(T\) is temperature, and \(\rho\), \(\mu\), \(c_{p}\), and \(k\) are the density, dynamic viscosity, heat capacity, and thermal conductivity of the fluid or solid components, respectively. The thermos-physical properties used in the above equations are summarized in Table S1.  
The boundary conditions include a fixed flow rate and temperature at the inlet of the water jacket and fixed pressure and fully developed temperature at the outlet of the water chamber. The no-slip boundary condition is applied at the internal surfaces of the water chamber. The temperatures and heat fluxes are coupled at all internal surfaces of the system. The free convection boundary condition was applied across all external surfaces of the system, which are exposed to the surrounding environment, as given below:  
External surfaces of the system:  
\[k_{surface}\;\frac{\partial T}{\partial n}=h_{air}\;\left(T-T_{air}\right) \tag{13}\]  
where \(n\) is the vector normal to the surface, as schematically shown in Figure 1.  
Geometry was created and divided into fine elements using Gambit software. The mesh generated involved a combination of structured and unstructured elements.
# A Regional Brightness Control Method for a Beam Projector to Avoid Human Glare
## 4 Implementation Details and Results
### Performance Evaluation
#### 4.4.1 Evaluation Methods  
In this study, the dimming area is determined by two segmentation models and the regional brightness control of the projector. In this section, we explain how to evaluate the proposed system's effectiveness and present some measured results from our implementation.  
To evaluate the system's effectiveness, a mannequin that could be recognized as a person was placed in front of the beam projector's screen. The measurements have been performed for two images resulting from two segmentation models: Selfie and DeepLab-v3. Each frame has been processed based on a size of 640 \(\times\) 360. In our experimental environments (shown in Table 2), the total execution time with the Selfie segmentation model appeared to be about 200 ms to process one frame. With DeepLab-v3, the total execution time for one frame appeared to be about 220 ms. In the installation process, our  
Figure 12: Brightness control area detection results: (**a**) segmentation result of DeepLab-v3; (**b**) segmentation result of Selfie; (**c**) DeepLab-v3 segmentation outline on masked image; (**d**) Selfie segmentation outline on masked image.  
implementation took 135 ms to generate the SSIM-MAP. Figure 13 shows a webcam image of the screen with a mannequin and the proposed system's output with DeepLab-v3.  
Segmentation is the task of labeling which classification each pixel belongs to. Usually, the performance evaluation of a classification model is carried out based on the confusion matrix. With the confusion matrix, the error rate of a system can be calculated by the following Equation (3) [36].  
\[Error\ Rate=\frac{FP+FN}{TP+TN+FP+FN} \tag{3}\]  
In the formula, \(TP\), \(TN\), \(FP\), and \(FN\) represent true-positive, true-negative, false-positive, and false-negative, respectively. In our study, the \(FP\) value indicates the number of error pixels that are not actually included in the human body, but the segmentation model determined they are. On the other hand, the \(FN\) value indicates the number of error pixels that are actually included in the human body, but the segmentation model determined they are not. The error rate of the system can be calculated using the rate of total error pixels, including both \(FP\) and \(FN\) pixels. Then, the system's accuracy can also be easily calculated by subtracting the error rate from 1.0.
## 1 Introduction
It was suggested that the applications of double-nanobeam systems are important in nano-optomechanical systems [24]. [PERSON] and [PERSON] [22; 25] analyzed the free bending and longitudinal vibrations of a double-nanobeam system (DCNTS) within the framework of nonlocal elasticity theory. Assuming that two nanotubes are identical, an analytical method for the forced vibration of an elastically connected DCNTS with a moving nanoparticle was developed by [PERSON] [26]. Moreover, The double-beam model has also been widely used in studying the dynamic response of bridge-rail systems under moving loads [27; 28; 29; 30; 31; 32]. [PERSON] and [PERSON] [33] used an infinite double-beam system under moving loads to simulate floating-slab tracks and investigated the vibration of the system, in which the characteristics of two beams were different. [PERSON] and [PERSON] [34] studied the vibration transmission from slab track structures into a bridge and used the finite-element method and multibody dynamics theory to solve the problem. The literature review indicates that the existing studies on the dynamics of double-beam systems have mainly focused on the dynamic response under free vibration and individual moving loads according to relatively complex calculation methods, but they have rarely evaluated the dynamic response of simply supported double-beam systems under successive moving loads in arbitrary spaces and sizes. A model was developed in this study for analyzing the dynamic response of a simply supported double-beam system under successive moving loads in arbitrary spaces and sizes; in order to reduce the difficulty of solving the equation, finite sin-Fourier transform was used to transform the infinite-degree-of-freedom double-beam system into a superimposed infinite number of a two-degrees-of-freedom system and obtain the analytical expression of its dynamic response. Based on a double-beam system under load-groups containing several moving loads, the analytical method proposed in this paper was used to calculate its dynamic response, and the calculation results were consistent with those obtained from the ANSYS finite-element numerical method, thus demonstrating the effectiveness of the analytical method proposed in this paper; some meaningful conclusions for engineering design were drawn as well. The analytical calculation method proposed in this paper for studying the dynamic response of a double-beam system under successive moving loads is briefer than previous methods and provides a theoretical foundation for further engineering applications of double-beam systems.
# A Mobile Solution for Enhancing Tourist Safety in Warm and Humid Destinations
## 5 Discussion
### Limitations and Potential Biases  
Several limitations and potential biases should be considered when interpreting the findings of this study. First, the sample size of our study was relatively small, which may limit the generalizability of our findings to other populations or contexts. While we attempted to recruit a diverse sample of tourists, our recruitment methods may have introduced selection bias, as we relied on convenience sampling and may have missed certain segments of the tourist population. Second, our study relied on self-reported data from tourists, which may be subject to social desirability bias or other forms of response bias. While we attempted to minimize these biases by ensuring anonymity and confidentiality, it is possible that some participants may have provided inaccurate or incomplete information.  
Third, the interpretation of our data may be subject to researcher bias, as our team members may have preconceived notions or expectations about the effectiveness of the Tourist Safety Support System (T3S). To minimize this bias, we employed a rigorous data analysis process that involved multiple team members and peer review. Fourth, our study did not include objective measures of tourist safety and security, such as crime statistics or accident reports, which may limit the validity of our findings. While we attempted to assess tourist perceptions of safety and security through surveys and interviews, it is possible that these perceptions may not accurately reflect the actual safety and security conditions at the tourist attractions. Fifth, our study focused on a specific geographic region and tourist population, which may limit the generalizability of our findings to other regions or populations. While we attempted to recruit a diverse sample of tourists, it is possible that our findings may not be applicable to other contexts.  
Finally, our study was conducted over a relatively short period of time, which may limit the ability to detect long-term effects or changes in tourist behavior. While we attempted to assess both short-term and long-term effects of the T3S, it is possible that longer-term effects may emerge over time. Despite these limitations and potential biases, we believe that our study provides valuable insights into the effectiveness of the Tourist Safety Support System (T3S) and its potential impact on tourist safety and security. We recommend that future research address these limitations and potential biases by employing larger sample sizes, more diverse recruitment methods, and more objective measures of tourist safety and security.
## 1 Introduction
### Motivations and Contributions  
In the era of the development of computer technologies and increased user mobility, the demand for intelligent systems (CPSs) that introduce amenities in our lives using various methods and techniques of AI is also growing. Considering that these systems use devices and sensors that process user data, it should be stated that CPSs must inspire users' trust and adapt to their needs. A special need and, simultaneously, a requirement for CPSs is security, which can be considered on many levels. People want to be safe on the way to work or the shop and do not want to become victims of car accidents. On the other hand, people expect technological security as well. As computer network users, people expect their data to remain intact or lost as a result of data transmission security errors.  
New solutions in the physical and environmental security field should contribute to increasing their users' safety levels. However, using AI methods may raise concerns about the effectiveness of these solutions and possible malfunctions. For this reason, regularly reviewing these solutions, their efficacy, and problematic issues is necessary, especially in TAI algorithms' safety and robustness dimensions. Thanks to this, it will be possible to improve solutions that require it.  
This review focused on TAI-based users' physical and environmental security solutions. We used these security types to describe the realizations and characteristics of the safety and robustness of TAI dimensions. We believe that studying the safety and robustness dimensions of TAI in users' physical and environmental security solutions can help readers understand the state-of-the-art theory and practice in this regard.  
First, we present the levels of vehicle automation. Next, we consider the work on detecting cars in traffic by different neural network approaches and how this affects autonomous vehicles. Then, we discuss pedestrian safety and address the issue of pedestrian detection using different methods. Next, we consider some works related to adversarial attacks on SNNs, DVS cameras, and bioinspired solutions to enhance adversarial attack resilience and TAI regarding Spiking Neural Networks' opportunities and risks. Also, we explain the safety problems and necessities in the IoT and sensor networks. We discuss the safety levels implemented by intrusion- and attack-detection systems. We analyze the challenges and requirements of the newly designed solutions connected with TAI methods.  
Also, we looked for other reviews related to Trustworthy Artificial Intelligence methods for users' physical and environmental security. We found reviews focused only on physical (for example, [17; 18; 19; 20; 21]) or environmental security (for example, [22; 23; 24; 25; 26]). These works mainly considered some subset of Artificial Intelligence methods and did not consider the trustworthy dimensions of AI.
## 2 Cepstrum Analysis for Wheel Flat Detection
### Cepstrum Analysis Technique under a Variable Velocity Condition  
The cepstrum is useful to detect periodic impulse signals at a constant speed. However, the cycle of vibration signal with a flat gets changed under variable velocity conditions of trains. This problem can be solved by an order analysis changing the time domain signal of vibration extracted from the axle box into the rotation angle domain signal of the wheel axis. In the case of railway vehicles, speed sensors and tachometers are always attached to the axle boxes to measure the rotational speed of axles, so no additional sensor is required for this domain change.  
Fourier transform based on the time domain would not be proper because the angular time intervals are not uniform at varying train speed unless the time domain is changed to the wheel rotation angle domain. For the order analysis, after exchanging the domains, interpolation must be applied to equalize the uneven angular intervals caused by the varying speed. Figure 7 is a block diagram of the current analysis that existing cepstrum analysis is combined and order analysis to effectively detect flats under operating conditions with variable speed.  
Figure 4: Separation of the wheel flat signal and the impulse signal in the Time, Frequency, and Quefrency domains.  
Figure 5: Bogie modeling.  
Figure 6: Flat wheel modeling.  
Figure 8 is a time domain signal extracted from simulation of wheel-rail dynamics at 2 km/h/s acceleration, and the speed was increased from 40 km/h to 50 km/h for 5 seconds. As shown in Figures 9 and 10, the time domain was changed into the rotation angle domain of the wheel axis, and the angular intervals were equalized using interpolation of signal data points. After that, the cepstrum was applied, and the flat signal detection was obtained as shown in Figure 11.  
Figure 8: Time domain signal of a wheel flat.  
Figure 7: The existing cepstrum analysis vs. the current cepstrum analysis combined with the order analysis.  
Figure 10: The conformability of the original data according to the number of data samples.  
Figure 9: Change of the time domain into the rotation angle domain.  
By the way, as the angle data intervals become narrower at the interpolation step, the original signal of the flat can be accurately recovered, and the frequency resolution is also improved. In addition, the flat cyclic signal component at every \(2\pi\) and the peak/mean value tends to increase. Figure 10 shows that the conformability to the original data is improved as the number of data samples increases using narrower angle data intervals.
# Analysis of Surface EMG Parameters in the Overhead Deep Squat Performance
## 2 Materials and Methods  
The analyzed muscles in this study were proposed by physiotherapists as the main muscles that should be activated in the overhead deep squat. There were three muscles in the lower part of the body, rectus femoris (RF), vastus medialis (VM), and biceps femoris (BF), and two in the upper part, deltoid muscle (D) and latissimus dorsi muscle (L). As well, the gluteus muscle (GM) was also considered in this analysis. In the next part of this article, we will use the term deep squat in place of overhead deep squat.  
The proposed method focuses on the statistical analysis of the chosen muscles' excitation expressed in the percentage of maximum voluntary contraction (%MVC) in reference to the level of correctness of the deep squat performance evaluated by the physiotherapists. In addition, the potential to differentiate the degree of correctness of deep squat performance based on the other sEMG parameters, such as mean frequency (\(MF\)), median frequency (\(MDF\)), kurtosis (\(kurt\)), and co-activation index (\(CAI\)), was also examined. Frequency parameters in the sEMG signal provide information about the frequency characteristics of muscle activity, and their analysis allows for a better understanding of muscle function and changes related to fatigue. The co-activation index, in turn, provides quantitative information about the simultaneous activation of muscles during movement, in particular agonist and antagonist. In Figure 1, we present the simplified workflow of this study.  
Figure 1: The simplified workflow of this study.
## 5 Discussion and Conclusions
The next phase of this study aims to evolve the proposed algorithm and approaches from a lossless framework to a near-lossless algorithm and to adapt them for application to 3D images, further enriching the literature with these advancements. Additionally, the DEA algorithm will be evaluated with different preprocessing techniques, aiming to develop a specialized compression algorithm for medical images. In conclusion, this study provides new insights into image compression, addressing both theoretical and practical aspects. The proposed algorithm and framework contribute to the development of more efficient and adaptable data management solutions in the digital era. Conceptualization, E.E. and A.O. ; methodology, E.E. ; software, A.O. ; validation, A.O. ; formal analysis, E.E. ; investigation, A.O. ; resources, E.E. ; data curation, A.O. ; writing--original draft preparation, E.E. ; writing--review and editing, E.E. and A.O. ; visualization, E.E. ; supervision, E.E. ; project administration, E.E. All authors have read and agreed to the published version of the manuscript. This research received no external funding. The study utilized datasets obtained from SIPI Image Database-Misc. ([[https://sipi.usc.edu/database/database.php?volume=misc](https://sipi.usc.edu/database/database.php?volume=misc)]([https://sipi.usc.edu/database/database.php?volume=misc](https://sipi.usc.edu/database/database.php?volume=misc)), accessed on 24 February 2025), Draper Satellite Image Chronology ([[https://www.kaggle.com/competitions/draper-satellite-image-chronology](https://www.kaggle.com/competitions/draper-satellite-image-chronology)]([https://www.kaggle.com/competitions/draper-satellite-image-chronology](https://www.kaggle.com/competitions/draper-satellite-image-chronology)), accessed on 24 February 2025), Chest X-Ray Images ([[https://www.kaggle.com/datasets/paultimothymooney/chest-xray-pneumonia](https://www.kaggle.com/datasets/paultimothymooney/chest-xray-pneumonia)]([https://www.kaggle.com/datasets/paultimothymooney/chest-xray-pneumonia](https://www.kaggle.com/datasets/paultimothymooney/chest-xray-pneumonia)), accessed on 24 February 2025), and Agriculture Crop Images ([[https://www.kaggle.com/datasets/aman2000](https://www.kaggle.com/datasets/aman2000) jaiswal/agriculture-crop-images]([https://www.kaggle.com/datasets/aman2000](https://www.kaggle.com/datasets/aman2000) jaiswal/agriculture-crop-images), accessed on 24 February 2025). The authors declare no conflicts of interest.