esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Computational Results of the Developed Algorithms
### Optimization of the Metaheuristics' Parameters  
To optimize the parameter values of the developed metaheuristics, the Taguchi method was used. The choice of these parameters has a significant effect on the efficiency of the metaheuristic algorithms. The parameter values that are needed for optimization act like controllable factors in the design of experiments. The aim is to find an optimal combination of the parameters such that the total flow time is minimized. To employ the Taguchi method, Minitab 19 was used; under the menu options, Stat-DOE-Taguchi Design Create Design was selected. For five levels of six factors, an L25 orthogonal array is used. This orthogonal array lists the different combinations of factors at different levels at which the response values of experiments have to be determined.  
The parameters of the genetic algorithm are: population size (PS), maximum number of generations (MNG), crossover probability (Pc), mutation probability (Pm), number of elite individuals (NEI), and the number of individuals on which local research is applied (NILS). The parameters of the iterated local search algorithm are: maximum number of iterations of the sequence operator (MISO), maximum number of iterations of the affectation operator (MIAO), maximum number of perturbations of the perturbation operator (MPPO), and threshold distance between solutions to reiterate the steps of the iterative local search algorithm (DOS). The optimum values of the genetic algorithm and the iterated local search algorithm parameters are given in Table 4.
# Estimating Underwater Light Regime under Spatially Heterogeneous Sea Ice in the Arctic
## 1 Introduction
[9] proposed a semi-empirical parametrisation that includes a second exponential coefficient in Equation (1) to model light decrease at the interface between the ice and ocean water at the bottom of the ice layer (Equation (3)):  
\[E_{d}(z)=E_{d}(0^{-})e^{-K_{d}(z)\ z}-(E_{d}(0^{-})-E_{d}(\text{NS}))\ e^{-K_{NS}(z)\ z} \tag{3}\]  
where \(E_{d}(0^{-})\) is the irradiance that would be observed under homogeneous snow or ice cover, \(E_{d}(\text{NS})\) is the irradiance under-ice, and \(K_{NS}(z)\) describes the decrease of \(E_{d}(0^{-})\) just under the ice layer. Both the methods by [PERSON] et al. [8] and [PERSON] et al. [9] make it possible to propagate local \(E_{d}(z)\) vertically under low transmission ice. However, these methods cannot identify and correct for inflated \(K_{d}\) when profiles are measured beneath an area of high transmission relative to surrounding areas. Additionally, when trying to determine primary production by phytoplankton that drift under sea ice and therefore are not static under sea ice features, what matters is the average shape of the vertical \(E_{d}(z)\) profile, which may possibly be predictable using a large-area \(\overline{K_{d}}\) as under a wavy open ocean surface [10]. In this study, using both in situ data and 3D Monte Carlo numerical simulations of radiative transfer, we show that the vertical propagation of average \(E_{d}(z)\), \(\overline{E_{d}}(z)\), is reasonably well approximated by a single exponential decay with a so-called large area \(K_{d}\), \(\overline{K_{d}}\), under sea ice covered in melt ponds. We further demonstrate that \(\overline{K_{d}}\) can be estimated from the vertical attenuation coefficient for upward radiance (\(K_{Lu}\)) because the latter is apparently less affected by local surface features of the ice cover. We implicitly assume that primary production can be adequately modelled using \(\overline{E_{d}}(z)\), and we conclude that \(K_{Lu}\) is an appropriate AOP for predicting the vertical variations in \(\overline{E_{d}}(z)\) under sea ice.
# SPR-Optical Fiber-Molecularly Imprinted Polymer Sensor for the Detection of Furfural in Wine +
## 2 Materials and Methods
### Measurement in a Drop  
The flat surface of the described optical platform makes it possible to perform the measurement in a drop simply deposited over the flat surface. The platform was fixed in a mini holder, which was purposely designed to keep the sensing surface, embedded in the resin block, in a flat position. A sample drop (50 \(\upmu\)L) was deposited over the flat part of the sensor, allowed to expand over the sensing surface and the support, and equilibrated for 5 min. During this time, the drop over the surface maintained its shape due to the MIP surface's hydrophobicity, mainly constituted by DVB.  
The concentration of 2-FAL in the sample only influenced the refractive index (RI) of the polymer in contact with the gold layer since the concentration of 2-FAL in the sample  
Figure 1: Optical-chemical sensor system: (**a**) outline of the experimental setup; (**b**) surface plasmon resonance- plastic optical fiber (SPR-POF) platform covered by a thin molecularly imprinted polymer (MIP) layer; (**c**) SPR-POF platform covered by a thick MIP layer. The grey block represents the resin support (1 cm \(\times\) 1 cm \(\times\) 1 cm) in which the POF is embedded.  
was too low to affect the solution's RI. Any RI variation related to the matrix, the so-called \"bulk effect\", had to be avoided or corrected.  
As schematically shown in Figure 1b,c, this aspect is particularly relevant when very thin MIP layers are considered, i.e., thinner than the plasmonic wave penetration in the dielectric. In that case, the \(\Delta\lambda\) must always be measured in solution with the same RI. If the sample and the standard solutions have the same RI, the spectra can be recorded directly in the drop of the liquid sample positioned over the sensing layer, but when RI is different, the solvent exchange method proposed for SPR measurements in serum (\(n=1.348\) RIU at 600 nm [37]) in flowing conditions [38] must be applied.  
In the static condition required by the determination in a drop here considered, a small volume of sample (40 \(\mu\)L) was dropped over the flat part of the sensor and equilibrated for 10 min as in the direct measurement method, but without recording the spectrum and measuring \(\Delta\lambda\). The sample solution was eliminated by suction, and the platform was washed with 40 \(\mu\)L of water. After the washing step, 40 \(\mu\)L of water was deposited over the MIP to record all the spectra with the same bulk liquid overlying the receptor layer. In this way, only the RI change induced by the binding of the analyte was detected, without any possible perturbation induced by the bulk refractive index of the examined sample. The transmission spectra in water were normalized to the spectrum obtained with the corresponding sensor (SPR-MIP) in air (reference spectrum).  
As a matter of fact, in the reference spectrum acquired on SPR-MIP with a thin MIP layer, no plasmon resonance was excited in the operative refractive index range of the platform here described [36].
## 4 Conclusions  
In this study, the electromagnetic force induced structural vibration characteristics of the BLDC motor for operating speed ranges are predicted using a 3-D entire FE model, and the results are validated through a modal test. The 3-D entire FE model is developed by including the stator core, rotor, permanent magnets, stator housing, fin, coil, and other various components. The natural frequencies are accurately predicted within a relative error of 3% compared to the modal test results. The mode shapes computed by FE analysis are consistent with the modal test results.  
For variable speed, the electromagnetic force induced vibration characteristics of the BLDC motor are predicted over the entire operating speed range by using the electromagnetic-structural vibration coupled analysis method with both the 3-D entire FE model and the simplified FE model. The harmonic order components of electromagnetic force, which induced a large amplitude of vibration, are calculated. The electromagnetic-structural vibration coupled analysis results are consistent with the vibration experiment results. In particular, the frequency that caused a large vibration on both the middle and high operating speed ranges can only be predicted well by using the 3-D entire FE model, which cannot be predicted using the simplified FE model. This research shows that the prediction of frequencies of peak magnitude that occur in the operating speed range is available by using the electromagnetic-structural vibration coupled analysis with a 3-D entire FE model.  
The electromagnetic-structural vibration coupled analysis method for various operating speed ranges with the 3-D entire FE model proposed in this study will contribute to improving the prediction accuracy of motor vibration characteristics over a wide range of operating speeds.  
Conceptualization, S.C. and C.-W.K.; methodology, S.C. and C.-W.K.; software, S.C. and K.J.; validation, S.C., K.J. and C.-W.K.; formal analysis, S.C.; investigation, S.C.; resources, S.C.; data curation, S.C.; writing--original draft preparation, S.C. and C.-W.K.; writing--review and editing, S.C., K.J. and C.-W.K.; visualization, S.C.; supervision, C.-W.K. and K.-D.L.; project administration, C.-W.K.; funding acquisition, C.-W.K. All authors have read and agreed to the published version of the manuscript.  
This paper was supported by the Technology Innovation Program (20012518) funded by the Ministry of Trade, Industry & Energy(MOTIE, Korea) and a National Research Foundation of Korea (NRF) grant funded by the Korean Government, Ministry of Science, ICT & Future Planning (No. 2019R1A2C1090228).  
Figure 14: Waterfall graph of the electromagnetic-structural vibration coupled analysis with the simplified FE model in the operating speed range.
# Research and Design of a Decentralized Edge-Computing-Assisted LoRa Gateway
## 5 Conclusions  
This article proposed the design and implementation of a LoRa gateway with edge computing, which is called an \"EC-assisted LoRa Gateway\". Then, an EC-assisted LoRa gateway prototype was developed on an embedded hardware system, and we proposed a new distributed computing model and used the latency-aware algorithm (LAA) for task processing at the edge nodes in the system, which effectively reduced the latency of task processing and improved the reliability and stability of the network. Experiments demonstrated the efficacy of our approaches, with the suggested LAA algorithm achieving the greatest edge node improvement and performance. We showed the highest performance that the EC-assisted LoRa gateway is capable of achieving: compared to a conventional  
Figure 6: System throughput and CPU utilization.  
Figure 7: Bandwidth occupation of the EC-assisted LoRa gateway system and traditional LoRa gateway system.  
LoRa system, the EC-assisted LoRa gateway reduces CPU usage and bandwidth use without compromising system throughput.  
In our future work, we plan to further advance research in edge computing. The current proposal did not address issues related to energy efficiency or the detection and repair of failures in edge nodes. In the future, these issues may be considered for achieving high network availability in the context of IoT ecosystems. Furthermore, the experiments presented in this paper were conducted using a simulated LoRa transmission interface, time, and rate, and not with actual massive LoRa end devices. Therefore, future experiments will focus more on the deployment of actual massive wireless modules and data testing to extend the research to a wider range of wireless transmission scenarios.  
Conceptualization, X.Z.; methodology, X.Z. and H.G.; software, X.Z.; validation, H.G. and H.Y.; investigation, H.G. and X.Z.; resources, H.G.; writing--original draft preparation, H.G. and H.Y.; writing--review and editing, X.Z. and Z.H.; visualization, H.Y. and X.Z.; supervision, Z.H.; project administration, Z.H. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
## 5 Smart Grid Characteristics
### Smart Meters and Distribution Automation  
A smart meter in the smart grid enables two-way communication between the meters and the utilities. The smart meters not only provide accurate bills but also let the customers monitor their energy consumption. The smart meter consists of sensors, power outage alerts, and power quality monitoring [40]. Distribution control is connected to the smart meter. Thanks to the advancement of advanced metering infrastructure (AMI), utilities can more easily collect customer information, which contributes to the improvement in grids where the integration of electric vehicles, converters, feed-in control, and fault isolation is possible. The substation automation system (SAS), which resolves power system congestion and restricts the integration of renewable energy sources less, is an example of distribution automation [41; 42].
## 2 Beam Dynamics Design
### Tolerance Analysis  
In practice, the distribution of input particles may deviate from the ideal state, prompting an investigation into the accelerator's ability to maintain optimal performance under such conditions. The impact of input beam mismatch on transmission has been systematically examined to assess the tolerance of the RFQ structure [35], as shown in Figure 6.  
Figure 6: Transmission efficiency versus (**a**) Twiss parameters (\(\alpha,\beta\)); (**b**) spatial displacements (\(\Delta\)x, \(\Delta\)y); (**c**) input beam transverse emittance; (**d**) input beam current; (**e**) input energy.  
Figure 6a reveals that, for a substantial range of input beam transverse Twiss parameters \((\alpha,\beta)\) near the design point, the transmission efficiency consistently exceeds 99.0%. In Figure 6b, the transmission efficiency is plotted against alignment errors in the input beam. To ensure stable operation, displacements in both \(x\) and \(y\) directions are preferably maintained within \(\pm\)2 mm. Figure 6c illustrates that, even with input transverse emittance as large as 1.60 mm-mrad, the transmission efficiency remains above 99.0%. Figure 6d outlines the sensitivity to input beam current, indicating that transmission efficiency surpasses 99.0% when the current is below 18 mA. Figure 6e demonstrates the transmission efficiency versus input energy of proton beams. Specifically, when the input energy ranges from 28.0 to 32.5 keV, the transmission efficiency remains consistently above 99.0%.  
The tolerance analysis yields positive outcomes, revealing that a reasonable deviation from the ideal input beam distribution has a negligible impact on transmission efficiency. Consequently, the beam can be stably accelerated. These results affirm the robustness of the SC RFQ design, paving the way for subsequent structural design.
# Harnessing the Cloud: A Novel Approach to Smart Solar Plant Monitoring
## 5 Artificial Intelligence in Solar Plant Monitoring
### Artificial Neural Networks  
In the literature, the application of AI, specifically supervised learning techniques empowered by Artificial Neural Networks (ANNs), has been reported by [25]. ANNs comprise interconnected nodes (neurons), which have similar structures and functions to the human brain. ANNs make the data suitable for analyzing complex data by learning the patterns and relationships in the data. These models are trained on relevant parameters and historical data and hold great promise for accurate fault diagnosis, classification and prediction, predictive maintenance, performance projection and optimization, and performance degradation of the solar plants [24; 55].  
The authors in [38] have proposed a solar plant monitoring algorithm using ANN for output power prediction of heterogeneous plants. Various parameters, like solar panel power, temperature, voltage, and solar irradiance have been considered for feature selection and used for training of the neural network-based models [56]. The algorithm classifies a solar panel as faulty if the predicted power output deviates by more than 10% from the actual output; otherwise, it is classified as no-fault. The predicted power output is used on a baseline to detect potential faults, underperformance, or deviating behaviors in the system [57].  
Additionally, convolutional neural networks (CNNs) are mainly used in the image processing domain; however, CNN has also been used in the monitoring of remote solar plants [58]. CNNs extract features from images, which can be helpful in locating faults, performance estimation, soiling on the panels, and shadowing effects [59; 60].
## 3 Results and Discussion
### Material Characterization
For example, the bare silicon wafer shows the expected O 1s, O 2s, O Auger, Si 2s, Si 2p, and C 1s signals, where the C 1s signal is attributed to adventitious carbon (surface contamination). After sputtering of carbon, a drastic increase in the C 1s signal is observed, while the Si 2s, Si 2p, and O 1s signals decrease substantially. The C to O atomic ratio (ca. 7:1) obtained from the survey scan of the sputtered carbon coating suggests a significant amount of sp\({}^{2}\) character in the upper 5-10 nm of the material, which is the sampling depth of XPS. This is consistent with the C 1s narrow scan of the material, which basically shows a single peak with significant asymmetry (a tail toward higher binding energy) (see Supplementary Materials Figure S2). Strong Si 2s, Si 2p, and O 1s signals are again observed after PDMS deposition on the carbon surface. The advancing WCAs are consistent with the expected changes in the material, changing from ca. 12\({}^{\circ}\) for the bare silicon surface, to 65\({}^{\circ}\) for the carbon-coated surface, to 133\({}^{\circ}\) for the PDMS-coated surface (see Figure 3, which also shows the static and receding contact angles for these surfaces). These results suggest that the bare carbon surface has both sp\({}^{2}\) character and C-O containing functional groups. The PDMS stationary phase presumably then adds more functionality to this material. Figure 1: Top-view SEM images of sputtered carbon deposited at throw distances of (**a**) 20 cm, (**b**) 12 cm, (**c**) 4 cm, and (**d**) with PDMS on top of the 4 cm coating. Scale bars are 1 \(\upmu\)m.
## 3 The Present: 2010-2015
### Applications of UPS and UBPS
#### 3.3.5 Strain Measurement  
As the H-UBPS method is capable of reconstructing a periodic (sub)surface structure with excellent accuracy, it should also be feasible to detect any change in surface parameters due to strain. Instead of machining a periodic surface structure, which would mechanically weaken the sample, we simply exploit residual surface roughness features. The ultrasonic strain measurement technique has been demonstrated on cold-rolled DC06 steel coupons, which have a deterministic surface structure left from skin passing of the work rolls (see Figure 13). The surface structure has an average roughness \(R\)a = 1.1 \(\upmu\)m with standard deviation \(\sigma_{\text{Ra}}\) = 0.0502 \(\upmu\)m, and has a clear degree of aperiodic features superimposed. The DC06 steel samples have been plastically strained at different levels ranging from 2% up to 35%. One can clearly observe a positional shifting of the backscatter spikes when strained (see Figure 14), from which the applied in-plane strain field was reconstructed [36].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Corrugation Parameters** & **Design Values** & **Ultrasonic Reconstruction** & **Optical Measurement** \\ \hline \(\Lambda_{1}\) & 250 \(\upmu\)m & 249.8 \(\upmu\)m & 250.1 \(\upmu\)m \\ \(\Phi_{1}\) & 100\({}^{\circ}\) & 99.3\({}^{\circ}\) & 99.2\({}^{\circ}\) \\ \(\Lambda_{2}\) & 375 \(\upmu\)m & 375.3 \(\upmu\)m & 374.8 \(\upmu\)m \\ \(\Phi_{2}\) & 10\({}^{\circ}\) & 9\({}^{\circ}\) & 9.1\({}^{\circ}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Corrugation parameters: design (column 1), ultrasonic reconstruction (column 2) and optical measurement (column 3).  
Figure 12: Microscopic image of ablated 2D subsurface corrugation (**a**) and corresponding H-UBPS recording at \(f=5\) MHz (**b**).  
In addition, we also analyzed the response of ultrasonic broadband pulses at normal incidence, in order to detect any change in the stimulation condition of a thickness resonance (Lamb wave cut-off frequency). From this change, the out-of-plane strain component can then easily be obtained. The ultrasonically obtained strain values have been confronted with the results of conventional strain measurements techniques (manual micrometer, mechanical extensometer, mono- and stereovision digital image correlation), showing excellent agreement for a wide range of applied strain values [36]. Interestingly, the developed ultrasonic strain gauge is the only method that was able to determine a 3D local strain field in a single-sided and contactless manner.
## 4 Experiments and Results
### Experimental Setup
#### 4.1.3 Implementation Details  
The experiments were performed on the Windows10 operating system, 4 Gi video memory, NVIDIA GeForce MX450 graphics card, Intel(R)Core(TM) i7-1165G7 @2.80 GHz, and TensorFlow1.18 framework. The performance of the proposed model on the answer selection task was analyzed by conducting multiple sets of comparative experiments. The parameter settings are described in Section 4.4, along with the results of different parameter settings.
## 1 Introduction  
Flooding is a key networking operation in wireless multi-hop networks, used for the dissemination of data packets and able to operate in a dynamic network, even in the absence of (or not relying on) accurate information about the network topology. For wireless routing protocols (reactive, proactive or both), flooding is for acquiring or disseminating topological information. In its most basic form, flooding consists of requiring every router in the network to retransmit each received packet exactly once over all of its interfaces. This mechanism leads to the well-known broadcast storm problem [1], a consequence of collisions between concurrent and unnecessary retransmissions of the flooded packet. To counter this problem, more sophisticated mechanisms for efficient flooding have been proposed, implemented and standardized [2; 3; 4] to reduce the number of (redundant) retransmissions and, consequently, the amount of packet collisions occurring. Some of these proposals substantially improve the performance of flooding in wireless multi-hop networks.  
However, the elimination of redundant retransmissions is not sufficient for completely avoiding packet collisions. Even if no redundant transmissions are present, wireless flooding in self-organized, decentralized networks leads to packet collisions caused by the simultaneous transmission of adjacent routers over the same wireless channel. These collisions constitute an important source of packet losses in wireless flooding.  
Different approaches have been explored to address this issue and to minimize its impact in wireless multi-hop networks. Classic MAC (Medium Access Control) collision avoidance mechanisms [5; 6] are not suited to current wireless sensor scenarios and are unable to solve all possible cases of collisions (e.g., broadcast or multicast transmissions, collisions between non-neighboring routers). Recent research efforts [7; 8] have focused on other alternatives, such as the use of multi-channel assignments in wireless sensor networks. These approaches are able to reduce the problem of collisions in potentially dense networking scenarios, at the cost of adding an additional complexity layer (or relying on previous knowledge of the network topology) and renouncing the semi-broadcast capability of the wireless network.  
According to the IETF (Internet Engineering Task Force), the problem of packet collisions in a MANET (Mobile Ad hoc Network) can be further alleviated by introducing jitter (a small, random delay on transmissions) in the network layer. In RFC 5148 [9], the use of jitter is recommended for MANETs and wireless sensor networks as a simple collision avoidance mechanism for routing protocol control traffic, such as periodically scheduled packets or event-triggered packets in the Optimized Link State Routing (OLSR) protocol [10; 11] or for MANET-enabled areas in OSPF (Open Shortest Path First) routing [12; 13].