esa-sceva/satcom-chunk-collection huggingface dataset:
# A Survey of the Real-Time Metaverse: Challenges and Opportunities +
## 8 Challenges and Opportunities
### User Experience
#### 8.5.2 Addressing Physical and Mental Health Risks  
The immersive technologies of the metaverse, specifically VR and AR, present significant physical and mental health challenges. _Motion sickness_, also known as cybersickness in virtual environments, is a significant UX issue in the metaverse [287]. It occurs when there is a disconnect between the visual input from the virtual environment and the user's physical sensations, leading to symptoms such as nausea, dizziness, and disorientation [67]. Furthermore, users face risks of injury or accidents due to attentional distractions, especially with AR applications that blend digital content with real-world environments [288]. Psychological risks include potential aftereffects like intensified emotional responses or confusion about reality, particularly for vulnerable users like children or those with certain mental health conditions [289]. The phenomenon of \"super-realism\" further complicates matters, as experiences in highly realistic virtual environments may blur the lines between virtual and real experiences, potentially causing users to develop emotional or cognitive disturbances when transitioning back to the physical world [290].  
To address cybersickness, developers can implement several strategies. Optimizing frame rates (ideally above 90 frames per second) is essential to ensure smooth rendering and reduce lag and visual inconsistencies, which are major contributors to motion sickness [291]. Likewise, minimizing latency in user inputs and head tracking (targeting less than 20 milliseconds) can help maintain a responsive experience, ensuring that virtual movements feel synchronized with physical actions. Designing predictable and smooth motion paths within virtual environments also helps reduce the likelihood of motion sickness. For example, rather than sudden shifts in perspective, developers can implement gradual transitions that simulate natural acceleration and deceleration, allowing users' senses toadjust to the changes in motion. Providing users with customizable comfort settings--such as adjustable field of view (FOV), camera smoothing, and snap-turning (e.g., a technique that reduces rotational motion)--can help users tailor their experience to their comfort levels, significantly reducing the risk of cybersickness [292]. Furthermore, teleportation (e.g., jumping from one location to another without continuous movement) has emerged as a popular locomotion method in VR that mitigates motion sickness by eliminating the disconnect between visual and physical motion. Offering multiple locomotion modes--such as teleportation, smooth walking, or vehicle simulation--allows users to choose their preferred method of movement based on their susceptibility to cybersickness [293].
# Cyberattacks and Security of Cloud Computing: A Complete Guideline
## 3 Service Models of Cloud Computing
### Scalability and Availability  
Scalability means the ability to develop systems and maintain performance. Scalability is the cloud layer's ability to increase software service delivery capacity by expanding the number of software services. A scalable system always supports its effectiveness at a high-level end and increases capacity [43]. In [44], the authors discussed two main topics, i.e., how to provide reliability, safety, and privacy of information and how to deliver scalable and robust cloud behavior under uncertainties and specific constraints, such as budgets, QoS, SLA, energy costs, availability, reliability, safety, and privacy. As more users use cloud technologies to build IT infrastructure, reliability, safety, and privacy become crucial for providers and consumers. Preservation of confidentiality is interpreted as limited access to information, integrity as the assurance that the data are trustworthy and accurate, and availability as a guarantee of reliable access to the information by authorized people.  
The technical metrics of scalability for cloud-based software service address both the quality and scaling of cloud-based software services. The work in [45] focuses on measuring the scalability of cloud-based software services. The proposed technical scalability matrix can be used to design and run cloud-based application services to identify system components that contribute to scalability performance. IoT/M2M/V2V devices connected to the internet are increasing day by day [46; 47]. These devices deployed to the cloud are overloaded with significant data traffic. To extend the scalability of IoT/M2M/V2V platforms in the cloud, the authors of [48] proposed utilizing fog computing. Fog provides low latency and also offloads over the congested cloud. The data are migrated to a fog computing architecture to make the whole platform more scalable in the proposed scheme [49].  
Availability is essential for many applications, including IoT applications. Services in the cloud system usually have the same availability level, in which fault identification and recovery procedures are unaware of the service features. Existing cloud systems are inefficient in improving availability methods and resources to meet service needs. A high-availability architecture is presented in [50] to address the issue of dynamically customizing the availability method based on service characteristics. Based on OpenStack, the proposed architecture is verified through an implementation system. It achieved the target availability while optimizing resources, unlike existing architectures that use predefined availability methods. The availability model presented in [51] is based on stochastic Petri nets to evaluate two approaches, i.e., hot standby and cold standby. In cold-standby migration, the migration target machine is started just before VM Live migration. The migration target machine operates with the source transfer machine in hot-standby migration. Results show that VM Live migration dramatically improved system availability.
## 5 High-Rise Building Optimal Evacuation Efficiency  
### Optimal Evacuation Efficiency in High-Rise Buildings  
During multi-story building evacuations, common practice is for the residents of the highest floors to use elevators while residents on lower floors use stairs for evacuation; however, this practice contradicts recommended scenarios C6: 2L3W4D5L and C21: 2L3L4W5L, where the fifth floor also uses stairs for evacuation. To study the efficiency of elevator use in high-rise buildings, considering the different efficiency trends of elevator-determined evacuation and staircase-determined evacuation with changing assistance ratios, the two evacuation modes are separated.  
1. Staircase-determined evacuation for floors four and five. Evacuation modes for the fourth and fifth floors are determined. Based on scenario C27: 2L3L4L5L, the evacuation modes for the fourth and fifth floors are changed to elevators in scenarios C24: 2L3L4D5L and C26: 2L3L4L5D. Changes in time are visually represented by comparing scenarios C24 and C26. The result is reduced evacuation times, as shown in Figure 7.  
Figure 7: Comparison of reduced times for C24 and C26.  
From Figure 6, it can be observed that the time saved by using an elevator for either the fourth or fifth floor is approximately the same; therefore, the impact on efficiency is roughly similar when considering the use of an elevator for either the fourth or fifth floor. The recommended evacuation plan should take into account the evacuation efficiency for the fourth floor.
2. Determination of fourth- and fifth-floor evacuation methods in elevator-determined evacuation. The analysis method for elevator-determined evacuation is the same as that for staircase-determined evacuation. In order for third-floor evacuation to be an elevator-determined scheme (C18: 2L3D4L5L), we modified the evacuation methods of the fourth and fifth floors using elevators. This led to the creation of two schemes: C15: 2L3D4D5L and C17: 2L3D4L5D. By comparing changes in scheme duration, we determined the efficiency of using elevators on the fourth and fifth floors. The shortened times for each floor using elevators are shown in Figure 8. From Figure 8, we can see that the elevator-determined evacuation scheme C15 yields the most significant time reduction; therefore, when adopting an elevator-determined evacuation, the recommended evacuation method for the fourth and fifth floors is 4D5L.
## 5 Experimental Scenarios and Results
### ADR-Det Performance Evaluation  
The fourth experimental scenario was based on a dataset of \(\approx\)86 K photos given to the authors by the motorway and tunnel operator. This large dataset was taken from the MAL-1H and MAL-1K stations, spanning the entire month of March 2021. Since two photos are taken from every vehicle, the software automatically stops the second photo from entering the detection pipeline. That way, identical results are prevented from appearing while at the same time doubling performance. Performance and measurements were recorded on the authors' reference HMRT-EF application, running only on software (not involving a GPU). The results are summarized in Table 6.  
As shown in Table 6, of the \(\approx\)86 K photos, half do not pass onto Stage 1. Of the photos entering the detection pipeline, 64.8% progress onto Stage 2, and only 2.81% make it onto Stage 3. Finally, 1.69% of the original, unique photos are OCR'd. The average processing time using a 6-cores/12 threads system is \(\approx\)0.45 sec/photo, yielding a rate of \(2.23\pm 0.1\) photos/sec. ADR-Det stage 1 utilizes, on average, about 90% of the CPU, while Stages 2 and 3 utilize about 80% of the CPU. Since the test system is equipped with a 6-core/12-Threads CPU, the detection processing rate is approximately 2.69 s per single-core/2-Threads for each photo and a speedup value of \(\sigma=1.56\) per core and \(\sigma=1.2\) per thread.
## 4 Experimental Results and Analysis
### Comparison with State-of-the-Art Methods  
This study undertakes a comparative evaluation of the accuracy of the proposed framework against state-of-the-art methodologies on the ISIC2018 Task 3 dataset. The pertinent outcomes are delineated in Table 10. Crucially, all enumerated SOTA methodologies utilize the identical experimental dataset, ensuring equitable and precise comparisons. Our proposed method attains a remarkable accuracy of 97.8% on the ISIC2018 dataset, surpassing the performance of alternative techniques in terms of accuracy (Acc), precision (Pre), and specificity (Spe). This enhancement underscores the superiority of our SkinSwinViT method over extant state-of-the-art technologies.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Technique** & **Acc (\%)** & **Pre (\%)** & **Spe (\%)** \\ \hline MobileNet+handcrafted features [36] & 92.4 & 92.1 & 90.0 \\ ResNet+Inceptionv3 [37] & 85.1 & 79.6 & 82.91 \\ Hybrid Model+handcrafted features [38] & 91.9 & 94.1 & 97.7 \\ Deep learning and moth flame optimization [39] & 90.6 & \(-\) & \(-\) \\ A CNN-based pigmented framework [40] & 91.5 & \(-\) & \(-\) \\ A CNN and nature-inspired optimization algorithm [41] & 91.7 & 92.4 & \(-\) \\ Two-stream CNN framework [42] & 96.5 & \(-\) & \(-\) \\ BF\({}^{2}\) SkNet model [43] & 97.1 & 95.1 & \(-\) \\ Proposed SkinSwinViT & 97.8 & 97.8 & 99.3 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Comparison of models trained on the ISIC2018 Task 3 dataset.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** & **Accuracy** & **Recall** & **Precision** & **Specificity** & **F1 Score** \\ \hline With Augmentation & 0.9783 & 0.9774 & 0.9781 & 0.9931 & 0.9777 \\ Without Augmentation & 0.9466 & 0.9456 & 0.9462 & 0.9669 & 0.9460 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Performance of using data augmentation or not.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**SkinSwinViT** & **Accuracy** & **Recall** & **Precision** & **Specificity** & **F1 Score** \\ \hline With Global Attention & 0.9788 & 0.9775 & 0.9783 & 0.9936 & 0.9779 \\ Without Global Attention & 0.9444 & 0.9391 & 0.9418 & 0.9612 & 0.9404 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Comparison of metrics on whether to use global attention.
# Mapping [PERSON] Using Sentinel-1 SAR Time Series in Camargue, France
###### Abstract  
This study proposes an effective method to map rice crops using the Sentinel-1 SAR (Synthetic Aperture Radar) time series over the Camargue region, Southern France. First, the temporal behavior of the SAR backscattering coefficient over 832 plots containing different crop types was analyzed. Through this analysis, the rice cultivation was identified using metrics derived from the Gaussian profile of the VV/VH time series (3 metrics), the variance of the VV/VH time series (one metric), and the slope of the linear regression of the VH time series (one metric). Using the derived metrics, rice plots were mapped through two different approaches: decision tree and Random Forest (RF). To validate the accuracy of each approach, the classified rice map was compared to the available national data. Similar high overall accuracy was obtained using both approaches. The overall accuracy obtained using a simple decision tree reached 96.3%, whereas an overall accuracy of 96.6% was obtained using the RF classifier. The approach, therefore, provides a simple yet precise and powerful tool to map paddy rice areas.  
rice; SAR; Sentinel-1; random forest; decision tree; classification +
Footnote †: journal: Computer Science
## 3 Influencing Factors and Interaction Identification of the Machined Surface Error
### Interaction of Influencing Factors of the Machined Surface Error  
The above analysis of variance results were calculated based on single-factor simulation results. The interaction between the design variables and the influence of the interaction on the positional and angular errors of the machined surface is yet to be revealed; therefore, the simulation scheme of error variation characteristics based on the analysis of variance results combined with the machined surface error solution is shown in Table 5.  
In the experimental design of the response surface methodology, response surface models are usually constructed based on second-order polynomials. Therefore, to resolve the response characteristics of the machined surface error to the design variables of the milling process, its second-order expression is constructed as shown in Equation (18):  
\[y=y_{0}+\sum_{i=1}^{n}p_{i}\cdot x_{i}+\sum_{i=1}^{n}\sum_{j=i}^{n}p_{ij}\cdot x _{i}\cdot x_{j}+\text{Q} \tag{18}\]  
where \(y_{0}\) is the initial to-be-determined value, \(p_{i}\) is the influence coefficient of \(x_{i}\), \(p_{ij}\) is the coefficient of the interaction effect of \(x_{i}\) and \(x_{j}\), and \(Q\) is the effect of fitting errors and noise, which obeys normal distribution in this model. Simulation of each experiment according to the model of solving the formation process of machined surface errors, using the error correlation of the machined surface positional errors and the machined surface angular errors with the design reference planes as the response result of the experiment, according to Equation (18), and using Design-Expert 13.0 Trial response surface model analysis software, along with response surface model construction for the simulation solution results of machined surface positional errors and machined surface shape errors, the mathematical model of the response surface methodology can be determined as shown in Equations (19) and (20):  
\[ \begin{array}{ll}\gamma(\Delta y)&=0.84-0.077n+0.002449f_{z}-0.001873M+0.004268 a-0.012n\cdot f_{z}\\ &+0.00579n\cdot M-0.013n\cdot a+0.00206f_{z}\cdot M+0.013f_{z}\cdot a\\ &+0.00069M\cdot a+0.00495n^{2}+0.00099f_{z}{}^{2}-0.00019M^{2}-0.01a^{2}\\ \end{array}  \tag{19}\]  
\[ \begin{array}{ll}\gamma(\theta)&=0.79-0.081n+0.00027f_{z}-0.00039M+0.0022a-0. 012n\cdot f_{z}\\ &-0.0501n\cdot M-0.05045n\cdot a+0.00057f_{z}\cdot M+0.052f_{z}\cdot a\\ &-0.013M\cdot a+0.00837n^{2}+0.031f_{z}{}^{2}-0.00467M^{2}-0.001596a^{2}\\ \end{array}  \tag{20}\]  
To determine the degree of influence of each design variable on the various characteristics of the machined surface errors, impact significance can be analyzed by response surface modeling of machining error distribution characteristics, and the impact significance level of each design variable can be determined. The probability levels (\(P\)) of the obvious terms of
###### Abstract  
This paper describes a comparative study of the adsorption of methylparaben onto commercial activated carbon and olive stones activated by calcination at 300 \({}^{\circ}\)C and treatment with 1 M HCl. The influence of the initial concentration of methylparaben, adsorbent dose, stirring speed and pH on the adsorption capacity of methylparaben on both adsorbents was studied. To find out the isotherm model, the kinetic model and the mechanism that best describe the adsorption process on each adsorbent, the experimental equilibrium data were analyzed using six isotherm models (Langmuir, [PERSON], [PERSON], [PERSON], Jovanovic and Dubinin-Radushkevich), and the experimental kinetic data were analyzed using four kinetic models (pseudo-first order, pseudo-second order, Elovich and Avrami) and two mechanistic models (Weber-Morris and Boyd). For both adsorbents, the Langmuir model best describes the adsorption equilibrium, the kinetics of the process follow a pseudo-first-order model and boundary layer diffusion is the step that mainly controls the adsorption process. The adsorption capacity of methylparaben on activated carbon is about four times higher than that of activated olive stones. The addition of graphene oxide and reduced graphene oxide to both adsorbents increases their methylparaben adsorption capacity, to a greater extent in the case of graphene oxide, being that increase more important in activated carbon than in activated olive stones.  
adsorption; methylparaben; activated carbon; activated olive stones; equilibrium; kinetics; mechanism; graphene-based nanomaterials +
Footnote †: journal: Applied sciences  
1
###### Abstract  
Purpose: This study aimed to explore the relationship between foot loading and comfort perception in two basketball shoes during basketball-specific maneuvers. Methods: Twelve male collegiate basketball players were required to complete three basketball maneuvers (i.e., side-step cutting, 90\({}^{\circ}\) L-direction running, and lay-up jumping) in two basketball shoe conditions (shoe L and shoe N, with different midsole cushioning types). Two Kistler force plates and a Medilogic inside plantar pressure system were used to collect kinetic data (i.e., impact force, peak loading rate, and plantar pressure variables). Perception scales were used to evaluate comfort perception. Results: No significant difference was observed between the two shoes during maneuvers in terms of ground reaction force. However, the plantar pressure of shoe L in the midfoot and lateral foot regions was significantly greater than that of shoe N during side-step cutting and lay-up jumping. Shoe N was significantly superior to shoe L, especially in dynamic scale in terms of the perception of comfort. The plantar pressure and perception characteristics in the two shoes were significantly different but inconsistent with each other. Conclusion: The biomechanical characteristics of the shoes themselves and the perception evaluation of the athletes should be considered in comprehensive shoe-cushioning design and evaluation.  
**Keywords: basketball shoe; comfort perception; foot loading; plantar pressure; maneuver**
## 3 Experimental Validation of the Mechanical Amplifier
### Mechanical Subassembly Testing
This unintended loading condition would compromise the accuracy of the recorded force values, affecting the overall validity of the experimental results. The results of the subassembly testing are presented in Figure 18.  
The DIC analysis provides a displacement map of the tested structure in the Z-axis direction, utilizing a color-coded scale ranging from 0 mm (red) to \(-\)0.228 mm (blue). Point 5 was included to ensure the correlation between the inductive transducer readings and the values obtained using the Aramis software. The measurement accuracy of the Aramis software in the XZ plane is \(\pm\)1 \(\upmu\)m, with the Mahr P2004 transducer utilized as the reference instrument. However, for the Y-axis direction, the recorded values are in the 30 \(\pm\) 6 \(\upmu\)m range. This deviates from the inductive transducer measurements, for which a maximum displacement of 5.02 \(\upmu\)m was observed. The discrepancy is due to the previously discussed limitations. Nonetheless, Y-axis displacements are not a primary concern, as they cannot be considered reliable under these testing conditions. Thus, the key results of this test are as follows:  
* Point 3 (upper arm tip) shows an X displacement of 0.159 mm and a Z displacement of \(-\)0.218 mm. * Point 2 (middle arm) shows an X displacement of 0.145 mm and a Z displacement of \(-\)0.068 mm. * Point 1 (lower arm) shows an X displacement of 0.035 mm and a Z displacement of \(-\)0.088 mm.
## 5 Conclusions  
Research of a continuous-time scheduling problem is conducted. We formalized the scheduling problem as a combinatorial set [8] of linear programming sub problems and evaluated typical computational procedures with it. In addition to the classical and estimated resulting conflict between the \"complexity\" and \"locality\" of optimization algorithms we came to the conclusion that the classical approach of randomization is unhelpful in terms of improving the locally found suboptimal solution. The reason here is that the scheduling problem in setting (1)-(3) has a very narrow feasibility area which makes it difficult to randomly detect a sufficient number of starting points for further local optimization. The efficiency of random search might be increased by introducing a martial rule or procedure of finding typical feasible starting points. The other effective global optimization procedures are mentioned in a very short form and are left for further authors' research. They are:* Genetic algorithms. From the first glance evolutionary algorithms [15] should have a good application case for the scheduling problem (1)-(3). The combinatorial vector of permutations \(c_{k}\in\{0,1\},\ k=1,\ldots,K\), seems to be naturally and easily represented as a binary crossover [15] while the narrow tolerance region of the optimization problem will contribute to the fast convergence of the breeding procedure. Authors of this paper leave this question for further research and discussion.
* Dynamic programming. A huge implementation area in global optimization (and particularly in RCPSP) is left for dynamic programming algorithms [16]. Having severe limitations in amount and time we do not cover this approach but will come back to it in future papers.  
The computation speed of the high dimension problem using an average PC is not satisfactory. This fact forces authors to investigate parallel computing technologies. Future research assumes adoption of created algorithms to a parallel paradigm, for instance, implementing map-reduce technology [17].  
This work was supported by the Russian Science Foundation (grant 17-19-01665).  
A.A.L. conceived conceptual and scientific problem setting; I.N. adopted the problem setting for manufacturing case and designed the optimization algorithms; N.P. implemented the algorithms, performed the experiments and analyzed the data.  
The authors declare no conflict of interest. The founding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.