esa-sceva/satcom-chunk-collection huggingface dataset:
# Stochastic Analysis of an Industrial System with Preparation Time Repair under Warranty Policy
## 3 Analysis of the System  
### Reliability  
The system reliability \(R(s)\) is the probability that the system will perform adequately for a given time period (\(0\), \(t\)). To derive system measures, we use the Markov process for the live states, i.e., \(\mathrm{S_{0}}\) and \(\mathrm{S_{1}}\). We first infer the reliability function according to the proposed equations.  
\[\left\{\frac{d}{dt}+\lambda_{1}+\alpha\right\}\pi_{0}(t)=0, \tag{1}\]  
\[\left\{\frac{d}{dt}+\lambda_{2}\right\}\pi_{8}(t)=\alpha\pi_{0}(t). \tag{2}\]  
The initial conditions are as follows:  
\[\pi_{i}(0)=\left\{\begin{array}{cc}1&i\
eq 0\\ 0&i=0\end{array}\right., \tag{3}\]  
We apply the Laplace transformation of Equations (1) and (2) and also use the initial condition, and we obtain  
\[\{s+\lambda_{1}+\alpha\}\pi_{0}(s)=1, \tag{4}\]  
Figure 1: State-transition diagram of the system.  
\[\{s+\lambda_{2}\}\pi_{8}(s)=\alpha\pi_{0}(s). \tag{5}\]  
From Equation (4), we obtain  
\[\pi_{0}(s)=\frac{1}{\{s+\lambda_{1}+\alpha\}}, \tag{6}\]  
By substituting Equation (6) into Equation (5), we obtain  
\[\pi_{8}(s)=\frac{\alpha}{\{s+\lambda_{1}+\alpha\}\{s+\lambda_{2}\}}. \tag{7}\]  
From the two Equations (6) and (7), we obtain the Laplace transformation formula for the reliability of the system as follows:  
\[R(s)=\pi_{0}(s)+\pi_{8}(s)=\frac{s+\alpha+\lambda_{2}}{s^{2}+(\alpha+\lambda_ {1}+\lambda_{2})s+(\alpha+\lambda_{1})\lambda_{2}}. \tag{8}\]  
If we use the inverse Laplace transform for Equation (8), we obtain the reliability function as follows:  
\[R(t)=\frac{\lambda_{1}-\lambda_{2}}{\alpha+\lambda_{1}-\lambda_{2}}\exp\{-( \alpha+\lambda_{1})t\}+\frac{\alpha}{\alpha+\lambda_{1}-\lambda_{2}}\exp\{- \lambda_{2}t\}. \tag{9}\]
## 3 Formulation of the Optimization Problem  
### Net Present Cost  
The objective function in the optimization model is the minimization for the Net Present Cost (NPC) which is the pillar factor considered for any project design; it is counted as a sum of all components costs including the capital \((C)\), operation and maintenance \((OM)\) and replacement costs \((R)\), considering also the fuel cost of the diesel \(\left({FC_{dg}}\right)\), taking into account the interest rate \((i_{r})\), inflation rate (\(\delta\)), and escalation rate (\(\mu\)) and the predefined project lifetime (\(N\)). The NPC modeling is expressed as follows [23; 24]:  
\[NPC=C+OM+R+FC_{dg} \tag{9}\]
# Model Mediation to Overcome Light Limitations--Toward a Secure Tactile Internet System
## 6 Tactile Internet System with AI Model Mediated  
Using model mediated system; a virtual model for the remote environment can be built on the edge cloud unit that is one communication hop away from the master device. The user interacts with the virtual environment and thus the delay is reduced to the distance between the edge computing unit and the end user. Figure 8 illustrates the structure of the tactile Internet system with the model mediated deployment. Figure 9 illustrates a time chart of the system, where \(\tau_{1}\) is system delay when the MEC unit is not deployed, \(\tau_{2}\) is the presence delay which is sensed by the master device, and \(\tau_{3}\) is the communication delay between the model mediated and the slave device.  
The model mediated system employs a predictor, which by some means of artificial intelligence (AI) can predict the behavior of the actual remote environment and be able to receive the haptic feedback information and use it to update the model [27]. The end user interacts directly with the virtual model on the edge cloud in a haptic/tactile form with an acceptable delay. Consequently, the user can get a real time experience of the remote environment through the AI-based model mediated deployed at the edge cloud unit. Since the tactile Internet requires a very high reliability, the model mediated system must employ a very high accuracy predictor and transparent modeling algorithms. As an initial step toward tactile Internet realization IEEE organization releases the IEEE 1918.1 standards group to standardize the tactile Internet [92]. The IEEE P1918.1.2 is a subgroup dedicated with the AI for enabling tactile Internet [93].  
Figure 8: (**a**) End-to-end general structure for the tactile Internet with the AI model mediated. (**b**) Signaling diagram for the tactile Internet system.  
_Stability vs. Communication Delay_  
One main issue that must be considered in designing the tactile Internet system is the system stability. The stability is mainly affected by the delay happened in communication network between the master and the slave devices. The more delay via the communication channel the more system disturbance that may lead to the system instability [94]. The AI-based virtual model at the edge computing connected to the master device will be affected by long delayed versions of haptic feedback even kinesthetic or tactile, also the model update which may depend on the visual feedback will be affected.  
Building an AI-based model mediated system for tactile Internet deployed on the edge cloud unit at the master side is the only way to overcome the light limitations and realize the tactile Internet system. The deployment of AI at the edge cloud leads to the perception of the near-zero delay networks as the master device will sense only the delay of the communication hop to the edge cloud. Many open research problems for such a system are concerned with the AI and the modeling of remote environment including physical properties and geometry.
## 2 The Principle of the Invariant Imbedding (IIM) T-Matrix Method
### T-Matrix Computation Based on the Invariant Embedding Technique
First, from Equation (32), we can know that:  
Figure 1:0Assuming that \(\overline{\overline{\mathbf{F}}}(n|r_{j})=\overline{\overline{\mathbf{F}}}(n-1|r_{j} )(\overline{\overline{\mathbf{I}}}+\overline{\overline{\mathbf{p}}})\) (\(\overline{\overline{\mathbf{p}}}\) is a matrix), then from Equation (34), it can rewritten as:  
Figure 1:1  
At the same time, we insert \(\overline{\overline{\mathbf{F}}}(n|r_{j})=\overline{\overline{\mathbf{F}}}(n- 1|r_{j})(\overline{\overline{\mathbf{I}}}+\overline{\overline{\mathbf{p}}})\) and \(\overline{\overline{\mathbf{g}}}_{n}(r_{i},r_{n})=i\overline{\overline{ \mathbf{I}}}(kr_{i})\overline{\overline{\mathbf{H}}}^{T}(kr_{n})\) (\(r_{i}<r_{n}\)) into Equation (31), a simplified expression between \(\overline{\overline{\mathbf{p}}}\) and \(\overline{\overline{\mathbf{q}}}\) can be obtained, written as:  
Figure 1:2  
where, \(\overline{\overline{\mathbf{Q}}}_{21}(r_{n})\) and \(\overline{\overline{\mathbf{Q}}}_{22}(r_{n})\) are another two auxiliary matrices, which are defined as follows:  
Figure 1:3  
Figure 1:4  
Then, by inserting Equation (41) into Equation (40), we can obtain:  
Figure 1:5  
The equation above can be regarded as a linear matrix equation of \(\overline{\overline{\mathbf{q}}}\), through which the matrix \(\overline{\overline{\mathbf{q}}}\) can be solved, written as:  
Figure 1:6  
At last, by substituting Equation (45) into Equation (36), the invariant imbedding iterative equation for the T-matrix can be obtained, given as:  
Figure 1:7  
From this equation, It can be seen that if the T-matrix \(\mathbf{T}(r_{n-1})\) of the sphere with \((n-1)\) layers is known, then the T-matrix \(\mathbf{T}(r_{n})\) of the sphere of \(n\)th spherical layers can be modified on the basis of \(\mathbf{T}(r_{n-1})\), and the correction terms mainly depend on the optical properties of the \(n\)th spherical layer (through the matrix \(\mathbf{Q}_{ij}\) (\(i,j=1,2\))).
# Sediment Dynamics of the Neretva Channel (Croatia Coast) Inferred by Chemical and Physical Proxies
## 1 Introduction  
The legal instruments of EU's Environmental policies and their regional and local application levels provide an interrelated regulatory framework for protection, preservation and prevention of the European oceans [1; 2; 3; 4]. According to these European directives, they are intended to apply an ecosystem-based approach to manage human activities whilst ensuring sustainable use of marine goods and services (respectively) [4; 5]. In particular, the Marine Strategy Framework Directive (MSFD) [3] uses eleven descriptors with several indicators covering ecological, physical, chemical and anthropogenic components of the ecosystems that need to be integrated to achieve a good environmental status [6]. In this context, the study of sediment dynamics plays an important role, as it provides the basic knowledge for a correct evaluation of the environmental quality.  
The Adriatic Sea is an important sub-region of the Mediterranean marine area and has been proposed as Ecologically or Biologically Significant Areas in the Mediterranean (EBSAs) from the Barcelona Convention, UNEP/MAP [7]. Several studies were carried out on sediment dynamics and geochemistry in the Adriatic Sea, but they were mainly focused on its western side (ItalianEconomic Water Zone; see [8; 9; 10; 11; 12; 13; 14] and reference therein). Hence, few geochemical data are available from sedimentary deposits of the eastern coast [15; 16; 17] and potential input of contaminants is still little known.  
Regional geology largely controls the composition of marine sediments; however, in densely populated regions, such as coastal areas, the anthropogenic influence may strongly affect the dispersion and concentration of organic matter and contaminants, modifying the river discharge through a combination of factors such as urban settlements and roads, runoff of agricultural soils, and dry and wet atmospheric deposition [18]. In this work, the influence of the Neretva River (NR) on the southern Croatian Adriatic coastal system was investigated in order to gain information on sediment dynamics of the area (Figure 1).  
The major aim of this work was to determine sediment pathways and dynamics of particles that bond metals in the Neretva Channel (NC) by combining sediment geochemistry, geochronological data and physical parameters of the water column to obtain basic information required for an assessment of the health and quality of the environment to preserve the marine life [19]. These results may provide a useful support for evaluation and decision-making processes aiming to achieve the environmental goals proposed by the Water Framework Directive [2] and the MSFD [3].
# A Virtual Learning Architecture Enhanced by Fog Computing and Big Data Streams
## 2 Distance Learning
### Some Still Unresolved Issues  
As shown in previous subsections, different interesting proposals of VLEs are present in the literature with great advantages towards the main stakeholders. However, some issues are still experienced by traditional and legacy e-learning platforms, such as small scalability, especially at the infrastructural level, poor efficiency in the dynamic resource usage and workload scheduling, software licenses and hardware costs, maintenance time and expenditures, etc. This is why some cloud-based distance learning platforms are emerging [24], and the cloud, be it public, private or hybrid, is becoming a cornerstone of any modern VLE.  
Nevertheless, some important research issues concerning the cloud and e-learning are still open [5]. They consider, for example, the way cloud infrastructures for education are built: currently, the Infrastructure as a Service (IaaS) layer is usually demanded from private middleware, while the Platform as a Service (PaaS) and Software as a Service (SaaS) layers are public services. Other challenges concern the need for easy-to-use scheduling and reservation functionalities, also in the presence of limited underlying private resources, the necessity of automatic scalability, according to the different periods of the year and of binding the monitoring metrics to the specific actions both at the IaaS level and at the PaaS and SaaS layers, the composition of complex learning modules, such as virtual laboratories and their integration in the cloud infrastructure, the interoperability between different learning services across different cloud standards, maybe in the form of Task as a Service (TaaS) or brokerage, and the support for mobile-learning capabilities such as ubiquity, synchronization, augmented reality, and so on. From the studied state of the art and the summarized open challenges, some needs emerge for improving current VLEs: the adaptation to particular learning situations, interactivity and real-timeliness, the closeness to reality, as well as authentic and real-world experiences, a trustworthy technical design, a layered adaptable and distributed architecture made of different modules for different granular analyses, careful attention to the coupling layers, more security, privacy and interoperability, maintenance of the traditional cloud layers and support for mobile learning and ubiquitous learning, as well as for context-aware and personal educational experiences.  
In the following sections, we are going to introduce the basics of big data streams and fog computing that will enable us to propose an innovative e-learning architecture that can meet the aforementioned requirements.
## 4 Discussion
### Comparison to Decoring Theory  
The best models achieved a mean MAE of 16 to 20% of the dataset's mean decored mass. With only a handful of datapoints for each sand-binder system, this can be adjudged a very good fit. The containing features in the data are suited to predict the target feature. The remaining important features after complexity reduction shown in Figure 16 are the flexural strength, compressive strength, phi, and cohesion, all at room temperature, combined with the absolute and relative mass at the start of the decoring process. This collection supports the claim of the previous work that these features describe the decoring behavior of sand-binder systems.  
The signal data have no major influence, as discussed above. The theory of decoring explained in [2] suggests that the way of impact is a significant influence. Therefore, the signal data should have a beneficial effect on model scores. This effect cannot be seen, which leads to the explanations already given in the context of the second hypothesis, or that the signal data were processed in a way that is not suitable for extracting the relevant information. Another explanation is that the unintentional variations during the decoring experiments were insignificant for the decoring behavior, which indicates a robust experimental setup of the previous work.  
According to the decoring theory described in [2], cohesion and phi are calculated using the measured flexural and compressive strengths. The best models use all four features and deem them equally important. If both sets contain the same information, at least one of the four should be filtered during the complexity reduction. All four are still deemed valuable for the modeling, which leads to two possible explanations. First, the models divided the information equally between the two sets, and they are still the most important compared to other features. Second, the sets are related, but each contains information not transported by the other.  
The extended features, such as the strengths of the sand core after casting, were deemed insignificant by the models. A low effect of the extended features contradicts the conclusions of the previous work. These strengths are a major indication of the decoring behavior in the latter. None of the best models used these features. Instead, some of the models used the strengths at 400 \({}^{\circ}\)C. Two explanations are possible. First, the strengths at 400 \({}^{\circ}\)C already describe the behavior of inorganically bound sand cores for higher temperatures and during casting in a more direct way than the strengths after casting. Second, the low number of datapoints for the extended features did not allow the models to learn the additional information contained in the extended features.
## 2 Methods and Principles
### Response Displacement Method
#### 2.1.2 Longitudinal Response Displacement Method  
In this study, a beam-spring combined model is utilized to simulate the precast immersed tunnel segments and the surrounding soil. Precast immersed tunnels are modeled with beam elements, while the joint areas of the immersed tunnels are represented using spring elements. In this model, each node is equipped with soil springs in two directions, axial and transverse, along the tunnel axis to simulate the foundation soil layers, thereby forming a system for the analysis of the immersed tunnel-joint-foundation, as illustrated in Figure 2.  
Figure 1: Calculation diagram of response displacement method.  
Utilizing the theories mentioned above, this study employs SuperFLUSH/2D (ver 6.0) software to perform free-field nonlinear dynamical analysis, thereby obtaining the relative displacements of the surrounding soil under seismic conditions. These displacements are subsequently applied as static loads to the restrained ends of the ground springs at the responsive segments of the analysis model for calculation.
## 4 Experimental Results  
### Experimental Setup  
The dynamic benchmark generator, described in Section 2, is used to generate the DTSP test cases. The following three stationary TSP benchmark instances are obtained from TSPLIB (Available at [[http://comopt.ifi.uni-heidelberg.de/software/TSPLIB95/](http://comopt.ifi.uni-heidelberg.de/software/TSPLIB95/)]([http://comopt.ifi.uni-heidelberg.de/software/TSPLIB95/](http://comopt.ifi.uni-heidelberg.de/software/TSPLIB95/)) (accessed on 3 April 2023)): kroA100, rat575, and pr1002. The magnitude of change \(m\) is set to slightly (i.e., \(m=0.1\)), medium (i.e., \(m=0.25\) and \(m=0.5\)), and severely (i.e., \(m=0.75\)) changing environments. The frequency of change \(f\) is set to \(10n\) iterations (i.e., \(f=1000\) for kroA100, \(f=5750\) for rat575, and \(f=10,020\) for pr1002) to allow sufficient time for the ACO algorithms to converge--and it is scalable for each problem instance. For each type of DTSP (i.e., weights or nodes changes), a total of four test cases are generated from each static benchmark instance. Ten environmental changes are allowed for each DTSP test case.  
The modified _offline performance_[34] is used as the performance metric, which is defined as follows:  
\[\bar{P}_{offline}=\frac{1}{E}\sum_{t=1}^{E}\phi(s^{bs^{\prime}},t), \tag{9}\]  
where \(E\) is the number of observations taken, and \(\phi(s^{bs^{\prime}},t)\) is the solution quality value of the best-so-far solution since the last dynamic change.  
The common ACO parameters of \(\mathcal{MM}\)AS and P-ACO are set to \(\alpha=1\), \(\beta=5\), and \(\omega=25\). The evaporation rate of \(\mathcal{MM}\)AS is investigated with values \(\rho=\{0.1,0.2,0.5,0.8\}\), and it is set to \(\rho=0.8\). Furthermore, the exploitation strength of the decision rule is set to \(q_{0}=0.0\), and the initial pheromone trail value \(\tau_{0}=1/\rho C^{un}\), where \(C^{un}=\phi(s^{un},0)\) is the solution quality generated by the nearest-neighbor heuristic. The maximum and minimum pheromone trail limits are set to \(\tau_{max}=1/\rho C^{best}\) and \(\tau_{min}=\tau_{max}\Big{(}1-\sqrt[n]{0.05}\Big{)}/\Big{(}(\textit{cand}-1) \cdot\sqrt[n]{0.05}\Big{)}\), respectively, where _cand_ is the number of different choices available to an ant at each step. The frequency with which the best-so-far ant deposits pheromone occasionally is set for every 25 iterations [29]. The population list size of the P-ACO is investigated with values \(K=\{2,3,5,10\}\), and it is set to \(K=3\). Furthermore, the initial pheromone trail value is set to \(\tau_{0}=1/(n-1)\), the exploitation strength of the decision rule is set to \(q_{0}=0.5\), and the maximum pheromone trails limit is to \(\tau_{max}=1\)[35].
# Dog Identification Method Based on Muzzle Pattern Image
## 4 Results and Discussion
### Performance Evaluation
#### 4.2.3 Proposed CLAHE  
As the result of repeatedly applying CLAHE instead of applying it once as in the proposed method 1, the dog identification performance were improved for SIFT and ORB as shown in Table 1 (Proposed Method 2). The EER was reduced by 1.6% from 1.6% to 0% for SIFT, and by 4.1% from 5.9% to 1.8% for ORB compared to the proposed method 1. However, the EER was the same for the SURF which increased by 1.8% from 10.9% to 12.7% for BRISK. SIFT performed the best and it identified all the dog images correctly. In addition, ORB had the most improvement.  
The average number of good matches was increased in both genuine matchings and imposter matchings compared to the proposed method 1 for all feature extraction algorithms except for BRISK. However, the ratio of the increases was much larger in genuine matchings than in imposter matchings. The large increases of correct matches in genuine matchings reduced the false nonmatches and resulted in the reduction of the EER. This result showed that the proposed technique, the repetition of CLAHE, was effective for the improvement of the dog muzzle pattern recognition for all feature extraction algorithms except for BRISK.
## 4 Discussion
### Hydrogen Peroxide Scavenging Activity  
The absence of detectable H\({}_{2}\)O\({}_{2}\)-scavenging activity in the elderberry hydrolate is due to the absence of chemical compounds capable of catalyzing the breakdown of H\({}_{2}\)O\({}_{2}\) into H\({}_{2}\)O and O\({}_{2}\), as well as due to the inactivation of enzymes like catalase and other peroxididases during the steam distillation process, which holds significant implications for understanding the potential protective role of elderberry hydrolate against DNA damage. Catalase is a crucial enzyme responsible for catalyzing the breakdown of H\({}_{2}\)O\({}_{2}\) into water and oxygen, thereby preventing the accumulation of this reactive oxygen species (ROS) and mitigating oxidative stress-induced damage [33].  
The steam distillation process, involving elevated temperatures, may have resulted in the denaturation or inactivation of peroxididases present in the elderberries. This inactivation could explain the lack of catalase activity observed in the subsequent assay, where the elderberry hydrolate was tested for its ability to dismutase H\({}_{2}\)O\({}_{2}\)[34].  
In light of these findings, the absence of catalase activity in elderberry hydrolate suggests that other antioxidant mechanisms may contribute to its potential protective effects against DNA damage. The observed concentration-dependent reduction in DNA damage, particularly under oxidative stress conditions, implies that elderberry hydrolate may harbor bioactive compounds capable of scavenging free radicals and mitigatinggenotoxic effects. These protective effects on DNA may be attributed to the presence of other antioxidants, such as polyphenols or flavonoids, which could play a role in counteracting oxidative damage.  
The implications of elderberry hydrate as a protective agent against DNA damage, even without antioxidant enzymes, highlight its bioactive components' complex and multifaceted nature. Further investigations into the specific antioxidant compounds present in elderberry hydrolate, their mechanisms of action, and their potential synergistic effects will contribute to a more comprehensive understanding of elderberry's role in mitigating oxidative stress and preserving DNA integrity. These findings have promising implications for developing natural products with DNA-protective properties suitable for skin care applications and other industries.