esa-sceva/satcom-chunk-collection huggingface dataset:
_Article_  
**Flexural Vibration Analysis of Nuclear Fuel Rod Bundles Interacting with Surrounding Fluid**  
**Subjected to Pressure Wave**  
**[PERSON] 1, [PERSON] 2 and [PERSON] 3,***  
1 Department of Automotive Engineering, Korea National University of Transportation, Chungju 27469, Korea; [EMAIL_ADDRESS]  
2 ATF Technology Development Department. Korea Atomic Energy Research Institute, Daejeon 34057, Korea; [EMAIL_ADDRESS]  
3 Department of Mechanical Engineering, Hanyang University, Seoul 04763, Korea  
* Correspondence: [EMAIL_ADDRESS]; Tel.: +82-2-2220-0424 check for updates  
Received: 4 February 2020; Accepted: 20 March 2020; Published: 27 March 2020  
**Featured Application: This approach allows vibration analysis of the nuclear rod bundles with surrounding fluid using the spectral element method.**  
**Abstract:** The structural behavior of the nuclear rod bundles that consisted of cylindrical beams was predicted using the spectral element method (SEM) while considering the interaction with the surrounding fluid. Viscous fluid behavior was utilized in order to calculate the forces acting on the nuclear rod bundles from the incident pressure waves. The added mass and fluid coupling on the nuclear rod bundles were determined for the position patterns and gaps of each of the cylindrical beams. The pressure field from propagating waves in the surrounding fluid was calculated with respect to the boundary conditions of the surface of the vibrating structures. With the increasing number of nuclear rods and decreasing pressure wavelengths, the structural vibration of the nuclear rod bundles that were induced by the propagating forces affected the scattering events of the pressure field. The frequency response of the nuclear rod bundles from the pressure waves in the water exhibited smaller damping, because the incident pressure wave travels without fluid coupling due to the longer wavelength when compared with distance between rods. The proposed numerical method can be utilized for the detailed design for effective parameters of a supporting system to reduce the vibration of nuclear fuel rod bundles for safety control.  
**Keywords:** fuel rod bundles; vibration; fluid coupling; SEM
## 2 Review of PV Model and MPPT Control Methods
### Review of MPPT Control Methods  
MPPT algorithms search for the point of maximum power, represented by the pair (\(V_{PV_{MPP}}\), \(I_{PV_{MPP}}\)), within the plane \(V_{PV}\), \(I_{PV}\) containing the solar panel's voltage and current curves [7]. Figure 2 illustrates how these curves are non-linear and significantly influenced by cell temperature and sun irradiation [1].  
MPPT algorithms are generally tested under specific and often simplistic irradiance conditions [10]. In most scientific papers, only step variation in irradiance is applied; nonetheless, common MPPT algorithms lose their bearings under gradual irradiance variation. Therefore, to more accurately compare the approaches of interest, various patterns of irradiance variation are used in this article.  
The following MPPT algorithms are taken into account in the comparison.
# Solar Irradiance Forecasting with Transformer Model
## 2 Dataset and Methods
### Encoder  
The encoder part of the model is used to convert the solar irradiance, weather, and day of the year into the latent space position. The goal of the encoder block is to look for mutual space-time bonds in the historical input data and convert them to latent space, which is the input to the next part of the model. Latent space is expressed for each timestep and location independently, as shown in Figure 6.
## 7 Discussion  
WuR technology offers new opportunities for implementing the proposed On-Demand-IoT concept. OD-IoT enables switching from a traditional periodic communication mode to an on-demand communication mode, providing an optimum response to the needs of applications while considerably limiting the energy impact of data exchange. OD-IoT substantially reduces a device's communication link budget and therefore extends its lifespan while guaranteeing highly responsive data collection and analysis for higher-level applications in complex IoT architectures. The benefits of WuR-based OD-IoT are amplified by the concomitant use of energy harvesting and novel on-demand data collection techniques. The main areas of applications that can profit from OD-IoT include environmental data collection, asset tracking, and smart healthcare.  
WuR technology has many potential implications for the sustainability of IoT-based industrial systems at the environmental, societal, and economic levels, with a major impact on energy savings.  
By limiting energy consumption and increasing device lifespan, WuR holds great promise for improving the overall environmental sustainability in industrial systems, especially in the context of the large-scale deployment of IIoT devices.  
In addition, the benefits of WuR in IIoT applications can have an impact on the well-being and safety of workers. By improving the reactivity and energy autonomy of IoT body area networks, as discussed in Section 4, health and quality-of-life monitoring can be enhanced by WuR. This technology could therefore have a positive impact on social sustainability.  
In an economic context, through the extension of device operating life while limiting maintenance costs, thanks to improved autonomy, WuR can help reduce the total cost of ownership of IIoT devices. This technology could therefore satisfactorily meet the objectives of a sustainable economy. Finally, using OD-IoT as a conceptual framework for the use of WuR would make the most of its potential for implementation for a sustainable industry.  
However, few WuR technology solutions are currently available on the market, and studied industrial applications are still rare. As demonstrated by the results of our experimentation, some limitations and constraints must be overcome, such as the limited range due to interference susceptibility and low-sensitivity receivers. Further studies are needed to improve the technology and to assess its scalability for large-scale IoT deployment. Currently, fundamental and standardization studies are in progress, which, once mature, should enable the wider adoption of the technology and its underlying OD-IoT concept.
# Mobility- and Energy-Aware Cooperative Edge Offloading for Dependent Computation Tasks +
## 3 System Model
### Computation Model
#### 3.4.1 Local Execution  
If our optimization algorithm decides to execute task \(k\) locally at the UE, then the execution time is  
\[T_{k}^{l}=\frac{b_{k}c_{k}}{f_{k}^{\text{UE}}}, \tag{4}\]  
where \(b_{k}\) is the data size of task \(k\) in bits, \(c_{k}\) denotes the required computational CPU cycles per bit for task \(k\), and \(f_{k}^{\text{UE}}\)is the UE's CPU cycle frequency allocated to execute task \(k\). Based on Equation (4) and the effective switched capacitance \(e\), which characterizes the chip architecture [61], the UE battery energy consumption for local execution can be estimated as  
\[E_{k}^{l}=(f_{k}^{\text{UE}})^{2}eb_{k}c_{k},\ \ \forall k\in\mathcal{K}, \tag{5}\]  
whereby we set the default effective switched capacitance \(e\) values to \(e^{\text{UE}}=10^{-25}\) F and \(e^{\text{UH}_{i}}=0.8\cdot 10^{-27}\) F in our evaluations in Section 6. We set the decision variable \(x_{k}=1\) if task \(k\) is executed locally; otherwise, \(x_{k}=0\).
# NN-Based 8 FSK Demodulator for the Covert Channel
## 4 NN-Based 8 FSK Demodulator and Its Evaluation
### Channel Coder  
In our simulation, for both demodulators, we implemented the standard convolutional encoder and Viterbi decoder [28] rate of \(\frac{1}{2}\) with generator polynomial \(\text{G}=[171,\ 133]\) for constraint length 7 (Figure 14).
## 3 Results
### Technical Validation
#### 3.1.2 Effects of Object Size  
Next, we detail the performance of density-based representations under challenging training conditions with very few training instances, highly varying object size, or both. We do this on SAR data, using \(B=12\) bins over the range \(-24\ldots 0\) dB, to keep computational complexity manageable for extensive experimentation on all estimators.  
We compare three proper density estimators, GKDE and LGPDE, with two inference algorithms (MCMC and Laplace approximation) and restrict to a single choice of the classifier to streamline the results; the observations are similar for the other classifiers. Figure 7 shows the accuracies for these estimators as function of the size of the training data for three scenarios: _small parcels_ that only uses parcels of \(20\ldots 30\) pixels for training and evaluation, _large parcels_ that only uses parcels of \(90\ldots 100\) pixels for training and evalution, and _variable parcels_ that uses both small and larger parcels (range of \(20\ldots 100\) px). The main results are: (i) The problem is considerably easier if the objects are larger but already for the small parcels of only tens of pixels we comfortably beat the random baseline; (ii) The accuracy naturally improves when we get more training instances, but already relatively small number of approximately 30 parcels per class is enough for good accuracy; (iii) The representations are robust over parcels of varying size, shown by relatively high accuracy for the case that contains both small and large parcels; (iv) There are no clear differences between the three density estimators in terms of accuracy.  
Figure 6: Choice of the discretization bins. (**a**) Accuracy as function of the number of bins for S2 MSI data. (**b**) S1 SAR intensity (sigma nought, \(\sigma_{0}\)) in crop field pixels concentrate within bounds of \(-30\ldots 0\) dB.  
Figure 7: Effect of field parcel size (line style) on MLP accuracy for different estimators (line color). Confidence intervals omitted for visual clarity.  
Even though we did not observe a direct improvement in classification accuracy for the more advanced density estimator LGPDE, it has the advantage of explicitly modeling the uncertainty of the estimate and we can propagate it through the classification process for any classifier as explained in Section 2.2.2. To demonstrate this, Figure 8 shows the classification accuracy for the three different classifiers for a dataset of small parcels (\(20\ldots 30\) px), for both PE-C and PP-C. We observe that the PP-C approach that models the uncertainty offers a small but consistent improvement. Figure 9 shows that the resulting class probability distributions behave as expected--for small fields the uncertainty is better captured in the final class distributions.
## 5 Conclusions  
In this paper, we proposed a novel multi-scale kernel CNN with residual blocks based on PCA using spectral-spatial information for hyperspectral image classification. To reduce redundant spectral information, PCA is used in data preprocessing. Moreover, to improve the classification performance we combined spectral-spatial information by extracting a data cube and unfolding it into a two-dimensional. The classification block used in this paper is a multi-scale kernel CNN which can effectively extract the most important information from the HSI pixels. In particular, using multi-scale kernels can expand the receptive field and thus reduce the risk of overfitting. To make the network go deeper, two residual blocks were applied to the network. Experimental results reveal that the proposed method outperforms the method using spectral information only, and other methods proposed based on CNN in recent years, in terms of overall accuracy assessment.  
Conceptualization, Z.-Y.W., J.-W.Y., and S.-Q.X.; methodology, S.-Q.X., J.-H.S. and Q.-M.X.; software, Q.-M.X. and S.-Q.X.; validation, J.-H.S.; formal analysis, J.-W.Y. and S.-Q.X.;writing-original draft preparation, Z.-Y.W. and C.-F.Y.; writing-review and editing, Z.-Y.W. and C.-F.Y.  
This research was funded by the National Key R&D Program of China grant number 2016 YFC0502902, the National Natural Science Foundation of China grant numbers 61672335 and 61701191, Department of Education of Guangdong Province grant numbers 2016 KZDXM012 and 2017 KCXTD015, the Key Technical Project of Fujian Province grant number 2017H6015, Natural Science Foundation of Fujian Province grant number 2018J05108 and the Foundation of Xiamen science and Technology Bureau grant number 3502Z20183032.  
We would like to thank anonymous editor and reviewers for their valuable suggestions.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
HSI Hyperspectral Image PCA Principle Component Analysis CNN Convolutional Neural Network
## 4 Mobility Aware Duty Cycling Algorithm (MADCAL)
### Threshold Calculation with Factoring Employed  
Using node 15 again as an example, which has a distance of exactly 50 m from the path of the MSN. In the example shown in Figure 4 the interference range is 77.52 m which would give an initial factor calculated as thus:  
\[factor=\frac{distToCircle}{interDist}, \tag{2}\]  
which in this case would result in:  
\[\frac{50}{77.52}=0.645. \tag{3}\]  
Assuming a speed of 2 mps then speed is less than 10 mps, therefore the factor check is set to 0.5. This would be compared against the result of the initial calculation which, if less than the factor check, would result in the factor check value, 0.5, being assigned as the value of the factor, ensuring it can not be reduced by less than 0.5. However, in this case this is not necessary and the angle of the threshold would be reduced by a factor of 0.645 as shown in Figure 5.
# Analysis of Vulnerabilities That Can Occur When Generating One-Time Password
## 4 Materials and Methods
### OTP Mechanism
#### 4.1.2 Generation Algorithm  
This is a cryptographic algorithm used to generate OTP. This encrypts the association information to produce a 20-byte bit string [23]. The Korean OTP standard [24] introduces the cryptographic algorithm used in this step. Since the generation algorithm is deterministic, it generates the same ciphertext from the same association information. The cryptographic algorithms used should provide a security strength of at least 112 bits [21; 23; 25]. Different OTP values may be generated according to extraction algorithms in one same bit string. This process corresponds to the blue part of Figure 1.
# Demonstration of Diode-Pumped Yb:LaF\({}_{3}\) and Tm,Ho:LaF\({}_{3}\) Lasers
## 3 Continuous-Wave Laser Experiment Operation
The central wavelengths were 1029 nm  
Figure 4: Variation of the absorbed pump power with the injection pump power. Figure 5: The average output power as a function of the absorbed pump power. and 1018 nm, respectively. At a transmittance value of 2%, double peaks appeared with wavelengths of 1028 nm and 1033 nm. Because the sampling resolution of the spectrometer was 0.7 nm, the fine spectral components were not tested. However, our experimental results could reflect the information of the output spectra to a certain extent. After the position of the cavity mirrors was carefully adjusted, Tm,Ho:LaF\({}_{3}\) lasers were acquired in D2. The experimental results of the relationship between the absorbed pump power and the average output power are explained in Figure 7. For different output mirrors, it is obvious that the absorbed pump power and average output power were almost linear functions. At the absorbed pump power of 3.53 W, the maximum average output power was 574 mW and the slope efficiency was 18.5% under 2% transmittance by M2. When a higher transmittance of M2 of 5% was adopted, the maximum average output power and the slope efficiency were decreased to 416 mW and 14.2%, respectively. The power was tested using a power meter (30(150)A-BB-18, Ophir, Jerusalem, Israel). The spectra of the Tm,Ho:LaF\({}_{3}\) lasers, shown in Figure 8, were measured using a spectrometer (NIRQuest-512, Ocean Optics, Largo, Florida, FL, United States). The laser spectra exhibited two peaks with both of the two output mirrors used in the laser test. When the transmittance was 2% and the average output power was 574 mW, the spectra showed two peaks at 2043 nm and 2048 nm. At a transmittance of 5% and output power of 416 mW, the peaks were 2039 nm and 2041 nm. Figure 6: Spectra of three different output parameters. Figure 7: Average output power as a function of the absorbed pump power. According to the research, both Yb:LaF\({}_{3}\) and Tm,Ho:LaF\({}_{3}\) lasers were successfully obtained. However, the laser stability and the size of the fundamental TEM\({}_{00}\) mode were not measured. The Yb:LaF\({}_{3}\) and Tm,Ho:LaF\({}_{3}\) crystals were uncoated and vertically incident with LDs, which increased the loss of the laser cavity. Supposing that the crystal was doped to a higher concentration, coated with antireflection film, and placed at the Brewster angle, the experimental data are expected to reach a higher slope efficiency and average output power. Moreover, the orientation of the crystal, the polarization in the spectra, and the polarization of the laser radiation need to be researched in detail.
## 4 Conclusions  
In this work, we model the Fluid Structure Interaction (FSI) problem arising from the behavior of a particular Flow Energy Harvester (FEH) featuring an innovative Vibration Inducer (VI). The latter is shaped as a semitubular cylinder with the concave side facing upstream and makes the attached cantilever oscillate regularly in a steady flow. Such shape of the VI is aimed at: (i) exploiting the longitudinal drag force ii) allowing for the undisturbed upstream flow to impinge directly on the cantilever. Given such peculiar dynamics, a novel modelling strategy had to be devised, accounting for i) the additional bending moment induced by the longitudinal forces acting on the VI; (ii) the direct (i.e., not mediated by the VI) action of the fluid on the beam. The latter is modelled as the sum of a positional and a velocity term, whose governing parameters are calibrated against experimental results. The action of the fluid on the VI is estimated by means of a numerical CFD analysis, enforcing the usual quasi-steady assumption. The model is validated by comparing its outcomes with the ones yielded by further CFD analyses encompassing the simulated motion of the VI. The match, in terms of average power over a multiple of the swaying cycle, is remarkable, notwithstanding the adopted minimal modelling of the complex interaction occurring between the fluid and the beam. The proposed model is a valuable tool to analyse the dynamic response of such class of Flow Energy Harvester and significantly aid their design procedure. Future studies will be devoted to a possible refinement of the form of the terms accounting for direct fluid-beam interaction.  
Conceptualization, P.P.; methodology, G.S.; validation, V.L. and P.P.; formal analysis, G.S. and P.P.; investigation, V.L. and P.P.; data curation, V.L.; writing--original draft preparation, G.S.; writing--review and editing, P.P.; visualization, V.L.; supervision, P.P. All authors have read and agreed to the published version of the manuscript.  
The authors acknowledge funding from the Italian Ministry of Education, University and Research (MIUR), in the frame of the Departments of Excellence Initiative 2018-2022, attributed to the Department of Engineering of Roma Tre University.  
Not applicable.  
The data used in this study are available from the corresponding author upon reasonable request.  
The authors declare no conflict of interest.
# The Telocytes in the Subepicardial Niche
## 1 Introduction  
Telocytes (TCs) were proposed in 2010 as being stromal cells morphologically different from other interstitial resident cells [1]. They were previously considered interstitial Cajal-like cells (ICLCs) [2; 3; 4; 5; 6; 7; 8; 9; 10]. Telocytes are small-sized cells residing in stem niches; they project long, slender, and moniliform prolongations, which are known as telopodes [11; 12; 13; 14]. In recent years, TCs have been noted in almost all organs and tissues in the human body [15; 16]. To date, no selective marker has been found for TCs [17]. The immunophenotype of TCs 'is similar to that of interstitial, endothelial, smooth muscle, mast and haematopoietic stem cells and neurons' [18]. Increasing evidence strongly suggests that, rather than being a distinctive cell type, TCs are just a cell morphology [11] of other TC-like cell types (fibroblasts, immune cells, endothelial cells, stem cells, etc.) [19; 20; 21], or a morphology resulted if thin, flat, pancake-like cells are cut and documented on two-dimensional slices [22]. Nevertheless, TCs, as a group, could simply be the result of a lack of uniformity and consensus in the terminology relatingto CD34-positive stromal cells used by different research teams [23]. Telocytes do not appear in the _Terminologia Histologica_[24; 25].  
The cardiac lymph is drained through the initial lymphatics from the subendocardial plexus to [PERSON]'s subepicardial plexus by intramyocardial channels; recollecting vessels drain the subepicardial plexus into collecting vessels running alongside the major coronary branches [26]. When cardiac ICLCs/TCs were documented previously, they were not distinguished from lymphatic endothelial cells [26].  
Following our research on the general background of TCs, we decided to critically review and provide new immunohistochemical evidence on (sub)epicardial stromal niche inhabitants. Very few studies have attempted to characterize epicardial ICLCs/TCs in different species and by different methods [10; 27; 28; 29] or to test whether or not such stromal inhabitants of a niche supplied by the overlying mesothelium belong to [PERSON]'s subepicardial plexus. We found no previously existing evidence indicating a potential mesothelial origin of (sub)epicardial ICLCs/TCs. We therefore applied an epithelial and a lymphatic marker within a panel of antibodies that also included c-kit and CD34, commonly used markers for ICLCs/TCs [10; 30; 31; 32; 33], but also hematopoietic markers, to document potentially overlooked cells discrimination when TCs were found in the subepicardial niche.
## 2 Materials and Methods
### Industry Supplied Data  
Time and temperature profile data were sourced from an industry partner, detailing the harvesting, transport and cooling conditions of Romaine lettuce. Temperature probes were placed in harvest bins and the temperature was recorded from the time of harvest to the time of cooling. The time and temperature profile data were collected for 3 yr and 2 mo (February 2016 to April 2019). Of the collected datapoints, 5615 were for Romaine lettuce grown and harvested in the Salinas, California growing region, and 4623 for the Yuma, Arizona growing region. The data were analyzed in the R programming language [57] for summaries and probability distributions of the data. The temperature and times for harvesting, transportation and cooling were first checked for outliers and distributions using boxplots and histograms, respectively. Using the group function, the median, upper and lower quartile of these data were generated for the different locations. Differences in the time and temperature data for the two locations were evaluated using pairwise tests, and where the data were not normal, [PERSON]'s median test was used. Time taken to cool the lettuce at the cooling center was generated from the difference between the time at the start of cooling and at the end of cooling. Time taken from harvest to cooling was computed by calculating the difference between the time at the end of harvesting and the start of cooling.