esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Classification
### Communication Technologies
#### 3.6.4 Localization  
[PERSON] et al. [27] have developed a co-operative pedestrian localization system that can operate at 2.44 GHz and 5.768 GHz. It uses a special tag as a VRU device which can directly communicate with the vehicle's device without any infrastructure. This V2P system achieves a communication range up to 100 m. Scalability and latency performance of this system need to be researched further due to its requirement of fixed tag identification numbers.
## 6 Numerical Tests and Discussion
### Verification of the Proposed Methods
#### 6.2.3 Validation of DRCE-IS  
In this section, we validate the DRCE-IS method by comparing the values of LOLP and EPNS obtained by MCS, CE-IS [28] and DRCE-IS. Then the CC for wind farms will be evaluated by the DRCE-IS and the secant method.  
Relevant parameters of DRCE-IS are set as follows:  
1. \(n_{s}\) in Step 1 of the pre-simulation stage is 50,000.
2. \(\rho\) in the Step 4 of the pre-simulation stage is 0.05.
3. The relative error limit in the main simulation stage is set as 0.05 for both LOLP and EPNS.  
Based on the parameters above, we can assess the reliability of power systems with the four integrated wind farms, whose values are displayed in Table 5.  
Since the LOLP and EPNS values obtained by the three methods are remarkably close to each other under the same relative error limit 0.05, we conclude that DRCE-IS is correct. Besides, the sample size of DRCE-IS is smaller than that of CE-IS, because of the proposed DR technique. Hence, the DRCE-IS is validated.  
The convergence process of the three methods is depicted in Figure 7. The ordinate is the relative error of the LOLP computed by samples. With the increase of sample size, the errors of the three all decrease. One can perceive that the relative error of DRCE-IS is the first to decrease to 0.05, which is also consistent with the results in Table 5.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Copula model** & D-vine & C-vine & Normal & t \\ \hline
**ECP-CvM** & 6.9377 & 9.1743 & 203.8986 & 208.2088 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of ECP-CvMs of three copula models.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **LOLP/10\({}^{-4}\)** & **Sample Size** & **EPNS/MW** & **Sample Size** \\ \hline MCS & 1.9455 & 3,066,720 & 0.0214 & 7,574,343 \\ CE-IS & 1.9774 & 14,540 & 0.0209 & 8916 \\ DRCE-IS & 1.9549 & 9374 & 0.0213 & 4709 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Reliability indices of the modified IEEE-RTS 79 system with four dependent wind farms.
## 1 Introduction
The main contribution of this paper is a new CH rotation algorithm, which takes into account individual energy savings achieved by the smart sensor nodes that suppress the unnecessary data transmissions. The proposed CH rotation algorithm was implemented in a prototype of wireless sensor network. During experimental evaluation of the new algorithm, detailed measurements of real energy consumption and network lifetime were conducted. Results of these experiments have revealed that lifetime of the prototype sensor network is extended when using the proposed method in comparison with state-of-the-art CH rotation algorithms. The paper is organized as follows. Related works are surveyed in Sections 2 and 3 includes presentation of the proposed algorithm, which enables extending lifetime of the sensor network. Experiments and their results are described in Section 4. Finally, conclusions are given in Section 5.
# Just-in-Time Fluid Flow Simulation on Mobile Devices Using OpenVisFlow and OpenLB
## 1 Introduction
The objective is to facilitate the scanning of a simulation domain and provide real-time visualization of calculated results _just in time_, eliminating the need for precompiled simulations or specialized expertise. By seamlessly connecting physical sketches with real-time simulations, _paint2 sim_ strives to function as a digital twin for 2D fluid flow simulations. Our contributions include the integration of the Lattice Boltzmann-based library _OpenLB_ version 1.5 [18] into the mobile device, _just-in-time_ simulation and visualization, as well as stable simulations for most cases. The application _paint2 sim_ plays a pivotal role in advancing applied CFD by providing students and engineers with a user-friendly platform for quick insights into 2D fluid dynamics. The unique feature of generating _just-in-time_ simulations on mobile devices empowers users to swiftly visualize and analyze fluid behavior in real-time, enhancing the accessibility and efficiency of fluid flow studies. This technology has the potential to revolutionize how we teach and learn fluid dynamics, as well as how we design and optimize fluid-based systems. _paint2 sim_ has the potential to benefit a wide range of users and applications, from students learning the fundamentals of fluid dynamics to engineers designing complex systems in the chemical, aerospace, and automotive industries. The technology can also be applied in medical research and environmental studies, where fluid behavior plays a crucial role. By simplifying the simulation process and making it more accessible, _paint2 sim_ has the potential to democratize the field of CFD and encourage a wider range of users to explore the fascinating world of fluid dynamics. In the remainder of this paper, we delve into the method employed by _paint2 sim_, present the numerical results obtained through its implementation, and engage in a comprehensive discussion of these results. A user guide with a download link for _paint2 sim_ can be found in Appendix A.
# A Novel Hybrid Model for the Prediction and Classification of Rolling Bearing Condition
## 2 Theoretical Foundation  
### Variational Model Decomposition  
VMD is an adaptive, completely non-recursive variational mode decomposition model. The core idea of VMD is to determine the decomposed \(K\) IMFs, their corresponding center frequencies, and bandwidth by iteratively searching the optimal solution of the variational model. The constraint model formula of VMD is as follows:  
\[\begin{split}&\min_{\{u_{k}\},\{\omega_{k}\}}\sum_{k=1}^{K}\left\| \partial_{t}\left[\left(\delta(t)+\frac{j}{\pi t}\right)*u_{k}(t)\right]e^{-j \omega_{k}t}\right\|_{2}^{2}\\ & s.t.\quad\sum_{k=1}^{K}u_{k}(t)=x(t),\end{split} \tag{1}\]  
where \(x(t)\)is the original signal and \(\{u_{k}\}:=\{u_{1},u_{2},\cdots u_{K}\}\) and \(\{\omega_{k}\}:=\{\omega_{1},\omega_{2},\cdots\omega_{K}\}\) represent the set of all modes and the corresponding center frequencies, respectively. \(\delta(t)\) is the Dirac distribution, \(\partial_{t}\) is the partial with respect to \(t\), and \(K\) is the number of modes to be decomposed (positive integer). To solve the optimal solution of the constraint model, the augmented Lagrange function method is introduced to transform the constraint problem into an unconstrained problem, and its formula is as follows:  
\[\begin{split}&\mathcal{L}(\{u_{k}\},\{\omega_{k}\},\lambda):= \alpha\sum_{k=1}^{K}\left\|\partial_{t}\left[\left(\delta(t)+\frac{j}{\pi t} \right)*u_{k}(t)\right]e^{-j\omega_{k}t}\right\|_{2}^{2}\\ &\qquad\qquad+\left\|x(t)-\sum_{k=1}^{K}u_{k}(t)\right\|_{2}^{2} +\left\langle\lambda(t),x(t)-\sum_{k=1}^{K}u_{k}(t)\right\rangle,\end{split} \tag{2}\]  
where \(\alpha\) is the quadratic penalty term and \(\lambda(t)\) is the Lagrange multipliers. The alternate direction method of multipliers is adopted to settle the optimal \(u_{k}\), \(\omega_{k}\) and \(\lambda(t)\), which can be written as:  
\[\hat{u}_{k}^{n+1}\leftarrow\frac{\hat{f}(\omega)-\sum_{i<k}\hat{u}_{i}^{n+1}( \omega)-\sum_{i>k}\hat{u}_{i}^{n}(\omega)+\frac{\hat{\lambda}^{n}(\omega)}{2} }{1+2\alpha(\omega-\omega_{k}^{n})^{2}}, \tag{3}\]  
\[\omega_{k}^{n+1}\leftarrow\frac{\int_{0}^{\infty}\omega\left|\hat{u}_{k}^{n+1} (\omega)\right|^{2}d\omega}{\int_{0}^{\infty}\left|\hat{u}_{k}^{n+1}(\omega) \right|^{2}d\omega}, \tag{4}\]  
\[\hat{\lambda}^{n+1}(\omega)\leftarrow\hat{\lambda}^{n}(\omega)+\tau\Bigg{(} \hat{x}(\omega)-\sum_{k}\hat{u}_{k}^{n+1}(\omega)\Bigg{)}.\]where \(\hat{u}(\omega)\), \(\hat{\omega}\) and \(\hat{\lambda}(\omega)\) denote the Fourier transforms of each variable, and \(n\) is the number of iteration. Ref. [18] provided more details on the VMD algorithm.
## 3 Results and Discussion  
### Air Temperature during 2020 and Reference Years in Highly Populated Cities in Spain  
In Figure 3, the minimum, maximum and average temperatures are depicted. In addition, in Table 1, the comparison of a short period (2020 and, specifically, in March, April and May 2020) with long-term series (10 and 30 years, and average values in March, April and May) were summarized. The climate station located in Madrid registered average temperature values in 2020 of 16.05 \({}^{\circ}\)C, which were \(-0.7\)\({}^{\circ}\)C with respect to the 10-year data series and the same as the 30-year data series. During the lockdown in March and April, colder months were registered, and only a warmer May was found. In Barcelona, the average temperature recorded in 2020 was 17.09 \({}^{\circ}\)C, being up to 0.61 \({}^{\circ}\)C warmer than during the last 30 years and 0.18 \({}^{\circ}\)C warmer during the last 10 years. Contrary to Madrid, in Barcelona, March, April and May 2020 were warmer than the average. Malaga and Sevilla, both of which are in southern Spain, showed higher average temperatures. Paying attention to Malaga, only April 2020 was colder than the long-term series. On the other hand, Sevilla recorded higher temperatures in March, April and May. Finally, in Valencia, 2020 registered an especially warm year, with temperatures +1.21 \({}^{\circ}\)C (30 years of data). Additionally, from March to May, warmer months were found in 2020 when compared with the long-term data series.  
In general, an increase in air temperature in 2020 was found when compared with both the 10 and 30-year data series. This agrees with all the reports related to the IPCC panels (e.g., [30; 31]) from recent investigations related to climate change in Mediterranean cities and all over the world [32]. Our results agree with previous studies related to heat islands and aridity published in these cities (e.g., [33; 34; 35; 36; 37; 38; 39]), although we have to also consider that less commuting during the shutdown may have resulted in less heat waste, resulting in a slightly reduced urban heat island effect and thereby reducing local temperatures. Therefore, it is difficult to confirm that the COVID-19 pandemic and lockdowns had an impact on air temperatures in the highly populated cities in Spain in 2020 due to the reduction of human activities and, subsequently, transport. Therefore, responding to this continued increase, we state that policymakers should focus on developing more green policies if, as some authors affirmed, there is a clear correlation with COVID-19 spreadability [40; 41; 42].
# Trust and Mobility-Based Protocol for Secure Routing in Internet of Things
## 5 Simulation Study
### Experimental Setup
#### 5.1.2 Parameters Setting  
The reception ratio (RX) value is set to a variable range of 30-100% to illustrate nodes' lossy nature. The Transmission ratio (TX) set to 100% for all nodes, depicting a loss-free transmission as the introduction of losses was only needed at the reception end but not at the radio transmission end. For all devices, the transmission range was set to 50 m and the interference range as 60 m. The interference and transmission ranges are in accordance with the coverage range of the available sensor radios. The simulation was carried out for 60 min. Each node periodically sends a 46-byte packet to the sink, including 30 bytes data and 16 bytes frame header, that is every 60 s (1 min) and after an initial start-up delay of 5 s. For the RPL network to converge, the initial start-up delay was set to 5000 milliseconds (5 s), which is appropriate for the RPL network according to the convergence time of 10 to 100 nodes [25; 27].  
RPL convergence time is required for all the nodes to join the network. It can be observed from the simulation platform when the DAG is completely constructed, and all the nodes have joined the network. In this way, the packets considered for evaluation are the packets after network convergence. While creating a new simulation in Contiki, the developer can set the more start-up delay (ms) in advanced settings according to the requirements. ContikiRPL supports the storing mode of operation by default. Similarly, DIO doublings and DIO min is also kept by the default setting of ContikiRPL as 12 and 8, respectively.  
In our experiments, we follow the default RPL operation and use the trickle timer for trust update and monitoring process. Trickle timer algorithm uses a periodic interval for sending out DIO messages to conserve nodes' energy resources in the network. DIO min, in Trickle timer, is the minimum interval between any two DIOs. Similarly, DIO doublings determines when the maximum value of the interval is reached. SMTrust makes use of the trickle timers so as to bound the control packet overhead, that is, to reduce the number of control packets by eliminating redundant messages. The parameters' setting in the Cooja simulator is adopted from our benchmarks [25; 27; 26], with the justification that this setting is feasible to evaluate a trust-based routing solution according to an IoT application depicting a real-world scenario.
## 3 Results and Discussion
### Effect of Open-Cell Structure on the Microwave Absorption Performance
#### 3.4.1 Effect of Cell Number on Microwave Absorption Performance
However, when the number of cells continues to increase, the graphene lamella connecting cells disappears gradually, which decrease the transmission of electron and causes absorption ability to decrease again. Overall, the OCGA with moderate cells achieves the best microwave absorption property, and the ratio of DE to GO solution in emulsion preparation process is set to 1:4 in our next study.
## 3 Strain Gauge Location Optimization of an Aircraft Wing
### Preliminary Selection of Strain Monitoring Points  
The corresponding strain values of the original 996 strain gauge locations under 90 operational load situations are obtained via the finite element model. Moreover, the strain monitoring locations are arranged in decreasing order according to the mean absolute strain value, and some strain monitoring points with small strain values are removed. In addition, the Pearson correlation between the remaining strain monitoring points are calculated. Those strain monitoring points with a correlation coefficient larger than 0.99 are merged, which is also considered the minimum distance constraint. Finally, a group of 13 strain monitoring locations are determined in the first stage of strain gauge optimization, which is illustrated in Figure 6.
# Structural Tunable Plasma Photonic Crystals in Dielectric Barrier Discharge
[PERSON] et al., integrated the gaseous plasma elements into a microwave photonic crystal cavity structure to control the transmission properties [32]. All of these works contribute significantly to a rich conceptual framework of PPCs. In these studies, the tunable responses of PPCs are generally achieved by changing the plasma density or lattice constant of the plasma structures. However, it is still a challenge to fabricate more flexible plasma photonic crystals, whose geometric configuration including the symmetry, the lattice constant and the crystal orientation can all be conveniently modulated. This will provide additional tunability and find more practical applications for PPCs. In this paper, we provide an efficient method to realize a rich variety of reconfigurable plasma photonic crystals in dielectric barrier discharge (DBD) by self-organization of the plasma filaments. The symmetry, the lattice constant and the orientations of different plasma photonic crystals can be deliberately changed by simply varying the applied voltage. In particular, these plasma structures can be rapidly reconstructed, which allow for active control within seconds or minutes. Based on the experimental results, the band diagrams of different plasma photonic crystals under a transverse-magnetic (TM) wave have been studied and characterized. The results would offer inspiration for engineering novel tunable optical materials with exploitable properties and find broad applications in the manipulation of microwaves or terahertz waves.
# Modified 16-Quasi Log Periodic Antenna Array for Microwave Imaging of Breast Cancer Detection
## 3 Parametric Study
### Effect of the Length w\({}_{1}\)  
The variation of w\({}_{1}\) alters the bandwidth performance of the PLPA, as shown in Figure 2. The value of w\({}_{1}\) is changed by 1 mm increments from 10.5 mm to 13.5 mm, while keeping all other parameters constant. At 3 GHz, the matching is not great for the lengths 10.5 mm and 13.5 mm. In comparison to 11.5 mm, the return loss for 12.5 mm is not improved. As a result, the most optimized w\({}_{1}\) value is 11.5 mm, which provides superior matching and return loss characteristics. At lower resonant frequency, the reflection coefficient is around \(-20\) dB while at higher resonant it is \(-35\) dB.
## 3 Results and Discussion
### Application of Microencapsulated Probiotics in Apple Juice
**(A)**: 4 \({}^{\circ}\)C, fermentation, core-to-wall ratio 1:1; **(B)**: 4 \({}^{\circ}\)C, fortification, core-to-wall ratio 1:1; **(C)**: 4 \({}^{\circ}\)C, fermentation, core-to-wall ratio 1:1.5; **(D)**: 4 \({}^{\circ}\)C, fortification, core-to-wall ratio 1:1.5. **(E)**: 25 \({}^{\circ}\)C, fermentation, core-to-wall ratio 1:1; **(F)**: 25 \({}^{\circ}\)C, fortification, core-to-wall ratio 1:1; **(G)**: 25 \({}^{\circ}\)C, fermentation, core-to-wall ratio 1:1.5, **(H)**: 25 \({}^{\circ}\)C, fortification, core-to-wall ratio 1:1.5. WP: whey protein, DWP: denatured whey protein. The change of pH can be used as an indicator to monitor both fermentation and storage processes quickly. The pH changes of fermented and fortified apple juices with different ratios and formulations of storage at 4 \({}^{\circ}\)C and 25 \({}^{\circ}\)C for 10 weeks are illustrated in Figure 9.  
Figure 9: pH of fermented and fortified apple juices. **(A)**: 4 \({}^{\circ}\)C, fermentation, core-to-wall ratio 1:1; **(B)**: 4 \({}^{\circ}\)C, fortification, core-to-wall ratio 1:1; **(C)**: 4 \({}^{\circ}\)C, fermentation, core-to-wall ratio 1:1.5; **(D)**: 4 \({}^{\circ}\)C, fortification, core-to-wall ratio 1:1.5. **(E)**: 25 \({}^{\circ}\)C, fermentation, core-to-wall ratio 1:1; **(F)**: 25 \({}^{\circ}\)C, fortification, core-to-wall ratio 1:1; **(G)**: 25 \({}^{\circ}\)C, fermentation, core-to-wall ratio 1:1.5; **(H)**: 25 \({}^{\circ}\)C, fortification, core-to-wall ratio 1:1.5. WP: whey protein, DWP: denatured whey protein. Meanwhile the pHs of all fermented samples were approximately pH 4.0 and did not change significantly during storage (Figure 9A,C,E,G), whereas the storage time has significant effect on the changes of pHs of the probiotic apple juice. In the case of fortification, the pHs values dropped to approximately pH 4.0 when storage at 4 \({}^{\circ}\)C and 25 \({}^{\circ}\)C for 28 days and 14 days, respectively (Figure 9B,D,F,H). Similar results were reported by [PERSON] et al. in 2012 and [PERSON] et al. in 2016. They reported that the orange juices fermented with Lactobacillus rhamns GG stored at 4 \({}^{\circ}\)C for 30 days exhibited lower acidification compared to one stored at 25 \({}^{\circ}\)C for 12 days and it may result better sensory properties. Summarizing, while the ratios of core-to-wall, storage time and temperature as well as fermentation or fortification methods significantly affected the changes of pH of probiotic apple juices during storage, the formulation of the microcapsules does not have any.
## 1 Introduction
### Related Work  
Several initiatives in Europe, Asia, and North America aim to develop healthcare digital platforms with collaborative access tools to allow the exchange and sharing of information and knowledge wherever and whenever needed throughout the attention process. This type of frameworks and architectures will allow maximum quality and efficiency for patient's care, and to provide appropriate attention to the patient's condition and risks.  
Castilla and Leon, for example, implemented a digitalization of health services as a tool to increase the efficiency of the services and increase the security in the attention to patient [7]. A healthcare cyber-physical system assisted by cloud and big data is being developed in the department of computer science at Pace University in New York [8]. This system consists of a data integration layer, a data management layer, and a data-analytics service layer to improve the functioning of the healthcare system. In France, a group of researchers implemented a wearable knowledge as a service platform to cleverly manage heterogeneous data coming from wearable devices to assist the physicians in supervising the patient health [9]. Another interesting work was presented at the International Conference on Computational Intelligence and Data Science (ICCIDS 2018). The authors proposed a hybrid four-layer healthcare model to improve disease diagnostic [10]. In India, a centralized architecture for an end to end integration of healthcare systems deployed in the cloud environment was developed using fog computing [11].  
Medical organizations are investing more and more in developing a healthcare platform that integrates data, applications, business processes, and user interfaces to gain knowledge and useful insights for clinical decisions, drug recommendation systems, and better disease diagnoses. Some other examples of big data applications in healthcare can be found in healthcare monitoring, where data captured from wearable devices can assist providers in managing symptoms of patients online and adjust their prescriptions [12]. An analytical platform called \"MedAware\" has been developed to detect errors in medical prescriptions and clinical errors, reducing the hospital admission and readmission in real-time [13]. In the healthcare prediction field, a healthcare system called \"Gemini (Generalizable Medical Information analysis and Integration system)\" was developed to collect, process, and analyze large volumes of clinical data and apply machine learning algorithms for performing predictive analytics [14]. Other platforms have been implemented for genomics data analytics to generate predictions based on DNA molecular changes and mutations [15]. Another type of healthcare platform is related to the healthcare knowledge system, defined as the combination of clinical data and physician expertise to support clinical decision-making and diagnosis [16].