esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Virtual Networking for Microservice Platforms: L2S-M
### Problem Statement
Implementing services like the one depicted in this example is often performed in cloud environments through virtual links (i.e., virtual networks) that enable the isolated communication of different modules, which can only reach the corresponding peers of a particular virtual network. This enables the creation of links between functions similar to the ones that are used in NSes in the context of NFV, enabling the connectivity of microservices in schemas like the one shown in the upper part of the figure. Figure 1: CDN service in a traditional networking approach vs. flat networking. Unfortunately, due to the intrinsic behaviour of the networking model used in microservice platforms, an implementation of such characteristics is impossible to achieve. As it is depicted in the bottom part of the figure, microservice platforms with flat networking approaches do not isolate these components: all of them are able to see each other at the network level, usually through a set of networking agents in charge of forwarding traffic to the rest of the components deployed over the platform. Although these agents can usually discriminate traffic based on some protocols (normally application-layer based), they do not completely isolate these components between them since they all are reachable at the IP layer. With the drawbacks of flat networking approaches in microservice platforms in mind, this paper presents a solution that enables the use of virtual networking for the deployment of network services and verticals in cloud-native environments: L2S-M. L2S-M aims at the provision of secure link-layer connectivity to NSes in cloud-native environments. Instead of being developed as a full networking solution to replace established connectivity solutions in different microservice platforms, L2S-M provides a flexible complementary approach to allow containers present in a cloud to attach into a programmable data plane that enables point-to-point or multi-point link-layer connectivity with any other container managed by the platform, regardless of its placement inside the infrastructure. This programmable data plane relies on software-defined networking (SDN) techniques to ensure the isolation of the link-layer traffic exchanged between containers. Moreover, SDN allows the application of traffic engineering mechanisms over the programmable data plane based on several factors like traffic priority or delay.
## 4 The Seismic Performance Parameters of RCS Composite Joints Under Floor Effect Are Studied
### The Influence of Floor Concrete Strength on Seismic Performance of Nodes
#### 4.5.2 Curve Characteristics and Ductility  
The hysteresis curves of each joint model are evaluated considering the effects of different concrete strength grades in the slabs. Under cyclic loading, it is observed that with the increase in concrete strength, the bearing capacity of the joints shows a slight improvement, and the energy dissipation capacity is enhanced as well. Notably, the increase in bearing capacity during positive loading is more pronounced than that during negative loading.  
Figure 26: (**a**) Comparison of energy consumption coefficients of different slab reinforcement nodes; (**b**) Comparison of stiffness degradation of different slab reinforcement nodes.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Node Number**} & **Capacity Dissipation** & **Equivalent Viscosity** \\  & **Coefficient \(E\)** & **Coefficient \(h_{e}\)** \\ \hline FJD-0 & 3.05 & 0.485 \\ FJD-7 & 2.96 & 0.470 \\ FJD-8 & 3.15 & 0.502 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Comparison of energy consumption capacity of different slab reinforcement nodes.  
When studying the impact of different slab concrete strengths on the skeleton curve of prefabricated modular RCS composite joints, as shown in Figure 28, we found that during the early stage of cyclic loading, when the displacement is less than 20 mm, the skeleton curves of all joints are basically consistent, demonstrating a linear relationship between load and displacement. As the load continues to be applied, the joints enter the yielding phase, and the increase in the slab concrete strength leads to an increase in the slope of the skeleton curve. Additionally, for peak differences between positive and negative loading directions, the improvement in joint capacity under positive loading is more significant than that under negative loading. Increasing the concrete strength slightly enhances the joint capacity (model shows a 4.5-7.5% increase in positive loading), but weakens ductility (reducing by about 5.8-6.3%). The enhancement of slab strength has a more significant impact on the positive loading capacity and shortens the deformation cycle both during yielding and failure. Overall, ordinary concrete meets seismic requirements, while high-strength concrete has a limited effect, and design should balance cost and performance. These observations indicate that increasing the slab concrete strength can enhance the load-bearing capacity of prefabricated modular RCS composite joints when considering the combined effect of slabs to some extent.  
Figure 27: Comparison of failure modes of different floor concrete strength nodes. (**a**) Destruction morphology of node FJD-0; (**b**) Destruction morphology of node FJD-3; (**c**) Destruction morphology of node FJD-4.
# Heat Load Forecasting of Marine Diesel Engine Based on Long Short-Term Memory Network
###### Abstract  
High heat load on diesel engines is a main cause of ship failure, which can lead to ship downtime and pose a risk to personal safety and the environment. As such, predictive detection and maintenance measures are highly important. During the operation of marine diesel engines, operating data present strong dynamic, time lag, and nonlinear characteristics, and traditional models and prediction methods cause difficulties in accurately predicting the heat load. Therefore, the prediction of its heat load is a challenging and significant task. The continuously developing machine learning technology provides methods and ideas for intelligent detection and diagnosis maintenance. The prediction of diesel engine exhaust temperature using long short-term memory network (LSTM) is analyzed in this study to determine the diesel engine heat load and introduce an effective method. Spearman correlation coefficient method with the addition of artificial experience is utilized for feature selection to obtain the optimal input for the LSTM model. The model is applied to validate the ship data of the Shanghai Fuhai ship, and results show that the mean absolute percentage error (MAPE) of the model is lowest at 0.089. Compared with other models, the constructed prediction model presents higher accuracy and stability, as well as an optimal evaluation index. A new idea is thus provided for combining artificial knowledge experience with data-driven applications in engineering practice.  
diesel engine heat load; intelligent detection; long short-term memory network; prediction model; evaluation index
## 3 Results and Discussion
### Kinetics  
The kinetics of the photocatalytic degradation of MO using TiO\({}_{2}\) and its composites were evaluated (Figure 7; Table 2). The experimental data fitted the first-order kinetic model. The photocatalytic degradation of MO was improved by the addition of OLNCs on the surface of the TiO\({}_{2}\). This was attributed to the enhanced adsorptivity of the OLNCs, which resulted in adsorption-coupled photocatalytic degradation synergy between the ONLCs and the TiO\({}_{2}\)[28]. As with the conversion data, the kinetic results followed the same trends of faster rates in the order 10 mg \(>\) 20 mg \(>\) 30 mg \(>\) 50 mg \(>\) TiO\({}_{2}\). This can be attributed to decreased electron-hole recombination due to the addition of OLNCs [22]. The TC-10 photocatalyst was selected for further study, as it gave the best performance.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Photocatalyst** & **Equations** & \(\mathbf{R^{2}}\) & \(\mathbf{\mathit{k}\ (min^{-1})}\) \\ \hline TiO\({}_{2}\) & Ln(C/C\({}_{o}\)) = \(-0.0051\mathrm{X}-0.0395\) & 0.8745 & 0.005 \\ TC-50 & Ln(C/C\({}_{o}\)) = \(-0.007\mathrm{X}-0.3511\) & 0.9728 & 0.007 \\ TC-30 & Ln(C/C\({}_{o}\)) = \(-0.00106\mathrm{X}-0.4488\) & 0.9307 & 0.001 \\ TC-20 & Ln(C/C\({}_{o}\)) = \(-0.0146\mathrm{X}-0.5349\) & 0.9229 & 0.015 \\ TC-10 & Ln(C/C\({}_{o}\)) = \(-0.05\mathrm{X}-0.1861\) & 0.8975 & 0.050 \\ TC-10 (H\({}_{2}\)O\({}_{2}\)) & Ln(C/C\({}_{o}\)) = \(-0.287\mathrm{X}+0.6119\) & 0.9002 & 0.287 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameters for the first-order kinetics for the photocatalytic degradation of methyl orange.  
Figure 7: First-order kinetics of the photocatalytic degradation of methyl orange.  
The photocatalytic degradation of MO was investigated in the presence of 5% H\({}_{2}\)O\({}_{2}\) (Figure 8). It can be seen that complete degradation of MO was observed in 30 min of light irradiation (Figure 8a), with a first-order kinetic rate of 0.287 min\({}^{-1}\) (Table 2 and Figure 8b). The kinetic rate was six times faster than the 0.050 min\({}^{-1}\) that took place in the absence of H\({}_{2}\)O\({}_{2}\). The ability of H\({}_{2}\)O\({}_{2}\) to decrease the electron-hole recombination reaction could be because of the process of harvesting the electrons and producing more hydroxyl radicals [29].
# An Accurate Forest Fire Recognition Method Based on Improved BPNN and IoT
## 2 Materials and Methods
### Network Structure  
This section describes the improved BPNNFire algorithm. Figure 2 illustrates the early warning structure of the forest fire monitoring network. The upper part represents the image information regarding fire over the forest monitored by a UAV, and the image is recognized using deep learning technology. Finally, the forest fire image is recognized and predicted at the output layer. The lower part represents identifying forest environmental information through the WSN to provide a timely warning of forest fires. The collected data are stored in the database.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Category**} & **Number of** & **Training** & **Verification** & **Testing** \\  & **Dataset Samples** & **Set Samples** & **Set Samples** & **Set Samples** \\ \hline Fire image & 53,830 & 43,064 & 5237 & 5529 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Distribution of forest fire image datasets.  
Figure 1: Images in one forest fire dataset.  
The forest fire monitoring/early warning system is depicted in Figure 3, with a forest environment information collection node, UAV, camera, sensor, solar power supply module, BPNNFire algorithm and DCNN model deployment platform, and forest fire online monitoring system. The forest environmental information collection nodes deployed in the field were powered using solar panels. The nodes include various sensors, such as air temperature, humidity, soil moisture, illumination, and wind speed sensors. The forest environmental information monitoring node collects forest environmental data within its area and transmits data to the gateway node in multi-hop form to a remote server through the network node. The data in the server are connected to the forest fire monitoring/early warning system through the interface, which can display real-time monitored forest environment data. The UAV collects forest fire image data over the forest and transmits the monitored image signal to the BPNNFire algorithm and DCNN model deployment platform. The forest fire online monitoring/early warning system can display and process the monitored forest fire images online and promptly detect a forest fire.
# Identification of a Human-Structure Interaction Model on an Ultra-Lightweight FRP Footbridge
## 3 Experimental Campaign
### Operational Modal Analysis  
The vibration modes of the structure were identified by performing an operational modal analysis (OMA). Data recorded with 18 high sensitivity accelerometers placed vertically on the bridge deck was employed for the analysis. Figure 4a,b shows the location of the accelerometers (model PCB B12) and a picture of one of them. For the test, the sampling frequency was 5120 Hz, and the duration was 10 min. The first three vibration modes of the footbridge were identified using the data-driven Stochastic Subspace Identification (SSI-data) method, as programmed in MACEC [35] software. A low-pass filter with a cut-off frequency at 80 Hz was used, and a factor of 64 was applied to decimate the raw data.  
Figure 4c displays the stabilization diagram of the test with a maximum model order of 160 and the power spectral density of all the signals superimposed. An upper limit of 20 Hz was considered given no human-induced loads are prone to excite vibration modes  
Figure 3: FRP footbridge: (**a**) Simply supported structure. (**b**) Static test.  
above this value. The identified vibration modes of the structure are depicted in Figure 4d, and the corresponding natural frequencies (\(f_{s}\)) and damping ratios (\(\xi_{s}\)) are presented in Table 1. Mode 1 corresponds to the first vertical bending mode, whereas Modes 2 and 3 are lateral-torsional modes. The second and third modes seem to be similar since the accelerometers were vertically placed. However, the former is mainly controlled by the lateral movement of the stringers, whilst the latter involves an action of the stringer-deck system. This difference can be seen in the results obtained from the modal analysis using the FE model of the footbridge (see Section 4.1).  
Also, a free response test was carried out to characterize accurately the damping ratio associated to the fundamental vibration mode of the structure (\(\xi_{s1}\)). A hammer was employed to excite impulsively the structure at mid-span. After conducting ten impacts, the logarithmic decrement of the recorded and filtered data was computed, and a mean value of 1.55% was obtained, which was quite similar to the one obtained through the OMA.  
Figure 4: OMA: (**a**) Location of eighteen accelerometers. (**b**) Picture of accelerometer No. 5. (**c**) Stabilization diagram. (**d**) First three vibration modes of the structure.
# Research on the Coverage of Ultraviolet Communication Network in the Arbitrary Polygon Area
## 2 Preliminaries  
### UV NLOS Scattering Model  
Figure 1 represents the schematic diagram of the UV scattering channel. The UV source at the transmitting end (\(T_{X}\)) transmits a light signal into space at a beam divergence angle (\(\Phi_{1}\)) and a transmitting elevation angle (\(\theta_{1}\)), and the receiver (\(R_{X}\)) receives the signal at the field of view (FOV) angle (\(\Phi_{2}\)) and the receiving elevation angle (\(\theta_{2}\)). The transmitting beam and the receiving FOV form an effective scatterer (\(V\)) in the overlapping area in the atmosphere. The emitted UV signal reaches the \(R_{X}\) after being scattered by \(V\). Thus, the NLOS transmission of the signal is completed. According to the transceiver elevation angles, UV communication can be divided into three work patterns, as shown in Figure 2: NLOS-a, NLOS-b and NLOS-c.  
Figure 1: The UV scattering channel [11].
## 4 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{2}{c}{**Energy Consumption(n/bij0 with Variations in the No. of RNs**} & \multicolumn{3}{c}{**\% Effectiveness of Proposed FRGNWS Framework**} \\  & & & & **with Existing Frameworks** \\ \hline \hline \multirow{3}{*}{**No. of RNs \(\downarrow\)**} & **GA-Based** & **ABC-Based** & **FRGNWS** & **FRGNWS Framework** & **\% Effectiveness of** \\  & **Framework** & **Framework** & **With Framework** & **with ABC-Based** & **with GA-Based** \\  & & & & & **Framework** & **Framework** \\ \hline
6 & 13.4 & 10.9 & 4 & 63.30 & 70.15 \\ \hline
12 & 17.6 & 13.8 & 7.5 & 45.65 & 57.39 \\ \hline
18 & 25.9 & 18.9 & 9.4 & 50.26 & 63.71 \\ \hline
24 & 35.8 & 22.8 & 11.5 & 49.56 & 67.88 \\ \hline
30 & 48.7 & 33.5 & 13.6 & 59.40 & 72.07 \\ \hline \multicolumn{2}{c}{Avg. \% Effectiveness\(\rightarrow\)} & & & 53.64 & 66.24 \\ \hline \hline \end{tabular}
\end{table} 
Table 14: The average % effectiveness of the proposed novel framework for the energy consumption metric with variations in the number of RNs. of the available \(k\)-node-disjoint routes from each SN to BS. Therefore, the proposed novel framework represents efficient coverage along with effective connectivity in the network and consequently results in a lower delay when compared with existing frameworks. Furthermore, considering Figure 8b, though the energy consumption increases with an increase in the number of RNs, our proposed novel framework demonstrates efficacy compared to the existing frameworks for the energy consumption metric. More RNs in the network result in an increment in the energy utilization in the network, since the traffic increases in the network and this further results in various different activities inducing higher energy utilization. Table 14 exhibits the average % effectiveness of the proposed novel framework compared to the ABC-based framework and GA-based framework as 53.64 and 66.24%, respectively. The proposed novel framework is energy efficient. Minimum relay nodes are deployed for maintaining the effective coverage in the proposed framework. The remaining available energy of each node in the route along with the energy consumption of the edge between adjacent nodes in the route are the two main factors out of a total of six factors considered while selecting the most-reliable node-disjoint route from each SN to BS. Therefore, the proposed novel framework shows efficacy in effective utilization of energy in the network and results in lower energy consumption compared to existing frameworks, the latter lacking energy efficiency. Some recent studies [15; 16] critically investigated the theoretical analysis aspects and found that the proposed framework still outperforms the related research works. The effective performance of the proposed framework is due to its ability to handle NP-hard and NP-complete optimization problems simultaneously using metaheuristic optimization algorithms, which was not considered in the recent literature.
###### Abstract
Several causes of intensity inhomogeneity exist due to the MRI device (e.g., static magnetic field inhomogeneity, eddy currents compelled by field gradients, radiofrequency transmission/reception, and scanner calibration) or related to the human body regions to be acquired (e.g., position of the patient's head not centered in the scanner, and magnetic susceptibility differences between tissues, such as interface between bone or air-filled sinuses and brain tissue) [21]. It can be unnoticeable to the human eye, but such intensity inhomogeneity affects the automated quantitative analysis, which is often limited by the assumption that a tissue's voxel intensities remain similar throughout the image [21]. In neuroimaging, several methods have been proposed for BFC of brain MR images, namely, using prospective methods (e.g., based on phantoms [22; 23], multicoils [24], and special sequences [25]), or using a retrospective approach depending on the real image information or some a priori knowledge about spatial and/or intensity probability distribution (e.g., filtering methods [26; 27], surface fitting methods [28], segmentation-based methods [29; 30; 31; 32; 33], and histogram-based methods [34; 35; 36]). A popular method, considered the gold-standard, is the N4 ITK [37] algorithm developed within the open-source Insight Toolkit (ITK) software [38], an extension of the N3 algorithm [34], that assumed a simple parametric model for the bias field and used an iterative and multi-scale optimization approach, not needing a segmentation or a priori knowledge from the scanner or subject. Another approach, using a non-iterative multi-scale method [39], showed superior performance in less time compared with the gold-standard. Recently, deep convolutional neural network methods (e.g., ABCnet [40], BIASNet [41], DeepN4 [42]) have been proposed for BFC in brain MR images, showing advantages over traditional algorithms because of not requiring modeling signals and bias fields or adjustment of parameters. Despite being very flexible and efficient, these methods still have limitations in their implementation and need to be trained on large datasets to achieve proper generalization for BFC of neonatal MRI data. Therefore, the purpose of this study was to identify the impact that different preprocessing steps, namely, BE and BFC methods, have on RBV measurements from preterm neonates MRI scanned at TEA.
## 3 Experiments and Results
The distance between the communication module and receiving module was about 2 m. Consistent with the testing method of Figure 6, the controlled experiments were carried out, and the results are shown in Table 3. It can be seen that the 2.4 GHz wireless communication signal passed perfectly through the resonator and was received by the receiving module, with a probability of 100%. Therefore, the above experimental results fully verify that the proposed cross-meandering resonator can not only shield the power frequency electromagnetic field but also allow wireless communication signal transmission, which has not been explored before (Table S1 of the Supplementary Materials).
## Materials and Methods
### UV Absorption Spectroscopy (UIVAS)  
Figure 2a shows a schematic diagram of the UVAS method. The circular dichroism (CD) spectrometer (j-815, JASCO, Easton, MD 21601, USA) with a wavelength range from 190-350 nm was operated in a nitrogen environment. 4 mL deionized (DI) water inside a quartz cuvette (100-QS, Hellma Analytics, Mullheim, Germany) with a standard optical path of 10 mm was treated at 1.5 mm below the NTAPP device. Right after the plasma treatment, the cuvette was transferred to the CD spectrophotometer without perturbing the solution, and the absorption spectrum of plasma activated water (PAW) was measured. The spectral resolution is 0.025 nm, the scanning speed is 200 nm/min, and the band width is 1.00 nm. The RONS concentration was determined from UV absorbance spectra by using Beer-Lambert law [29; 30; 31; 32; 33]:  
\[\text{Abs}(\lambda)=-\log T=-\log\left(\frac{I}{I_{0}}\right)=\mathit{elc} \tag{1}\]  
The absorbance (Abs), was defined by Equation (1) where \(T\) is transmittance, \(I\) is the light intensity after it passes through the plasma treated DI water and \(I_{0}\) is the initial light intensity. And \(\epsilon\) is the molar absorptivity of the chemical species at a certain wavelength, \(l\) is the optical path length, and \(c\) is the RONS concentration. Concentrations for the three reactive species H\({}_{2}\)O\({}_{2}\), NO\({}_{2}\)\({}^{-}\), and NO\({}_{3}\)\({}^{-}\) were calculated using the curve fitting method.  
We plotted the obtained UV absorption spectrum using the reference spectra data of H\({}_{2}\)O\({}_{2}\), NO\({}_{2}\)\({}^{-}\), and NO\({}_{3}\)\({}^{-}\) through the following way. The MATLAB program was used, and the fitting is referring to the previous study of the [PERSON]'s group base on Figure A1[22; 23].  
1. At first, fitting the spectrum with H\({}_{2}\)O\({}_{2}\) reference spectrum of a region longer than 245 nm.
2. Then, 195 nm to 205 nm region is fitted with the NO\({}_{3}\)\({}^{-}\) reference spectrum.
3. Finally, the NO\({}_{2}\)\({}^{-}\) reference spectrum is used in the range from 205 nm to 215 nm.  
Figure 1: Characteristics of the NTAPP: (**a**) an experimental setup, (**b**) voltage and current profile during discharge (**c**) optical emission spectra of NTAPP (**d**) a gas temperature according to gas flow rates and NTAPP treatment distances. All measurements were repeated three times. (**e**) a photograph of NTAPP plume (**f**) images of plasma treated water surface inside cuvette according to the distance between NTAPP device and water.