esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Measurements of NiO/PANI/ZnNb\({}_{2}\)O\({}_{6}\) Composite's EMI Shielding Effect
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Sample** & **SE (dB)** & **Frequency (GHz)** \\ \hline \multirow{6}{*}{NiO:ZnNb\({}_{2}\)O\({}_{6}\) (wt. \% 25–75)/Anilin: 1/1} & \multirow{6}{*}{\(-\)10} & 0–0.51, \\  & & 0.78–1.81, \\  & & 2.69–3.8, \\  & & 5.56–6.51, \\  & & 6.65–7.1, \\  & & 7.34–8 \\ \hline NiO:ZnNb\({}_{2}\)O\({}_{6}\) (wt. \% 50–50)/Anilin: 1/1 & \(-\)33.94 & 5.64 \\ \hline \multirow{6}{*}{NiO:ZnNb\({}_{2}\)O\({}_{6}\) (wt. \% 50–50)/Anilin: 1/1} & \(-\)20 & 5.43–5.79 \\ \cline{2-3}  & & 0–0.29, \\ \cline{2-3}  & \(-\)10 & 4.74–6.52, \\ \cline{2-3}  & & 6.64–7.06, \\ \cline{2-3}  & & 7.34–8 \\ \hline \multirow{6}{*}{NiO:ZnNb\({}_{2}\)O\({}_{6}\) (wt. \% 25–75)/Anilin: 1/1} & \multirow{6}{*}{\(-\)10} & 0–0.36, \\  & & 3.16–4.43, \\ \cline{1-1}  & & 4.74–6.52, \\ \cline{1-1}  & & 7.31–8 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Shielding effectiveness of the epoxy-(NiO/polyaniline/ZnNb\({}_{2}\)O\({}_{6}\)) composites across the frequency range. In future research, the composite consisting of NiO/PANI/ZnNb\({}_{2}\)O\({}_{6}\) can be subjected to more comprehensive studies at broad frequency bands, including with chopped fiber-like additives or different polymers. In addition, it is necessary to focus on multi-material hybridization, nanostructuring, geometric innovations, and specific frequency optimization to improve the microwave shielding efficiency of these composites. For researchers investigating higher frequencies, such studies will also allow them to comprehensively investigate shielding effectiveness and reflection loss in specific radar frequency bands and other frequency bands.
## 3 The Materials and Methods
### Multispectral Event Fusion
This is followed by optical flow refinement, which further corrects for subtle micro-motions and dynamic texture mismatches. In the end, histogram matching is applied to harmonize the distribution of intensity values across different modalities, reducing spectral inconsistency. Once the alignment process is complete, all input channels are resized to a standard resolution of 224 \(\times\) 224 and undergo z-score normalization. This preprocessing pipeline plays a crucial role in promoting training stability and reducing biases that may arise from modality-specific variations. Figure 2: Visualization of the alignment and fusion process of RGB, NIR, and simulated event channels into a 6-channel tensor. Unlike many existing architectures that rely on late fusion--processing each modality in isolation before merging their features at a later stage--HUE-Net adopts an early-fusion approach. This design choice stems from recognition of the fact that identity-relevant cues are often not confined to a single modality but are distributed across spectral domains and tend to manifest as correlated patterns in the early layers of a network. By integrating modalities at the input level, HUE-Net enables the learning of shared low-level filters that can jointly encode cross-spectral relationships and spatio-temporal dynamics from the outset. This strategy enhances the model's ability to extract robust, multi-modal representations and offers practical advantages in terms of computational efficiency. Early fusion eliminates the need for separate processing branches, thereby reducing parameter complexity and improving inference speed--an essential consideration for deployment on resource-constrained edge devices.
# A Method to Reduce Non-Nominal Troposphere Error
## 4 VPL under Non-Nominal troposphere
### Simulation Results
#### 4.3.1 Simulation Options and Parameters  
Parameters in simulations are set as:  
* Location: Linzhi airport (E94.335338 N29.305495, 2949 m);
* Constellations: current GPS constellation and future global BDS constellation;
* Altitude of airplane: h = 30 m;
* Time constant of the smoothing filter: 30 s;
* Aircraft velocity: 72 m/s;
* Mask angle: 5\({}^{\circ}\); and
* Simulation interval: 10 s.
# Deep Learning Entrusted to Fog Nodes (DLEFN) Based Smart Agriculture
## 6 Conclusions  
Colossal volumes of multimedia data gathered by IoT devices in modern smart agriculture are utilized to improve QoL of farmers. DL has come to the fore as a group of important techniques with which to pick out useful information from huge volumes of data. Nevertheless, supporting DL on IoT devices is constrained by their restricted resources and energy. Generally, cloud computing has been regarded as a universal approach to this issue. However, there is concern about service delay by network congestion caused by huge data volumes generated constantly by modern IoT infrastructures. Hence, we proposed a DLEFN algorithm that performs some DL tasks on fog nodes in an FC environment. The proposed method aimed to accommodate the maximum number of applications requiring DL service by using the maximum available resources of fog nodes, and to reduce network traffic and burden on the cloud. Accordingly, the DLEFN was designed to decide the optimal layers of DL model for application tasks for each fog node, in order to accommodate the maximum number of DL applications. The algorithm decided the appropriate layers of DL model for each fog node, taking into account its available bandwidth and computational capacity. The evaluated results were compared with existing methods in the same experimental environment and indicated considerable improvements regarding bandwidth efficiency, capacity efficiency, number of allowed DL applications, and cloud overhead. Considerable gains of performance proved the positive effect of individual decision-making about available resources of fog nodes. Therefore, as a solution among existing issues, the proposed algorithm excellently supported fog nodes with dissimilar resource capacities. Finally, we can clearly claim that the proposed DLEFN algorithm is an efficient scheme to graft DL onto FC-based smart agriculture environments to enable efficient service without service delays due to network congestion.  
Conceptualization, K.L., B.N.S. and K.H.; Data curation, K.L.; Formal analysis, K.L.; Investigation, K.L. and B.N.S.; Methodology, K.L.; Software, K.L.; Supervision, K.H; Writing--original draft, K.L.; Writing--review & editing, K.L, B.N.S and K.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by [National Research Foundation of Korea(NRF) grant funded by the Korea government (MSIT)] grant number [2019R1F1A1042721] and [BK21 Plus project (SW Human Resource Development Program for Supporting Smart Life) funded by the Ministry of Education, School of Computer Science and Engineering, Kyungpook National University, Korea) grant number [21A20131600005].  
The authors declare no conflicts of interest.
# Megalos: A Scalable Architecture for the Virtualization of Large Network Scenarios +
## 2 Related Work and State of the Art
### Single Host Network Emulation Systems  
Several solutions have been developed for emulating networks on a single host.  
NetKit [12] is a tool capable of emulating arbitrary network topologies using User-Mode Linux (UML) virtual machines.  
Kathara [13; 14] is the spiritual successor of NetKit. It leverages on Docker containers, instead of UML virtual machines, to emulate network devices.  
MiniNet [15] is a virtual testbed and development environment for software-defined networks. Network devices are emulated using pure Linux namespaces. It provides an extensive Python API to interact with the virtual devices.  
EVE-NG [16] is a network emulation system that supports different virtualization technologies, either container-based (like Docker) or virtual-machine-based (like QEMU).  
Containerlab [17] is a network emulator based on Docker containers. Similarly to NetKit and Kathara, it provides a declarative way of defining arbitrary network scenarios using a configuration file. Moreover, it is able to run vendor-specific router images.  
For scalability reasons Megalos is conceived as a distributed network emulation system.
## 6 Numerical Results
### Yaw and Depth Trajectory Tracking  
In this first simulation scenario, the underwater vehicle simultaneous maneuvers in the yaw and depth dynamics. The signal reference for yaw dynamic is a sine function with an amplitude of 45 degrees and a frequency of 0.1\(\pi\) hertz given by  
\[\begin{split}\zeta_{1,\
u,\hat{\psi}}&=\frac{\pi}{ 4}\cos(0.1\pi t)\\ \zeta_{2,\
u,\hat{\psi}}&=-0.1\frac{\pi^{2}}{4}\sin(0.1\pi t),\end{split} \tag{54}\]  
and, for the Z axis, a sine function with an amplitude of 2 degrees and a frequency of 0.1\(\pi\) hertz is chosen:  
\[\begin{split}\zeta_{1,\
u,z}&=2\sin(0.1\pi t)+3,\\ \zeta_{2,\
u,z}&=0.2\pi\cos(0.1\pi t).\end{split} \tag{55}\]  
In this scenario, the disturbance signals induced for the dynamic model (8) are given as  
\[\begin{split}\tau_{\hat{\psi}_{d}}=& 0.7\sin(1.2t)\cos(2.5t),\\ \tau_{z_{d}}=& 14\cos(1.5t)\sin(3t).\end{split} \tag{56}\]  
In Figures 3 and 4, we present the results obtained for the trajectory tracking problem in the yaw and depth dynamics. We can notice from Figures 3a and 4a that the performance of the feedback controller is deteriorated as a consequence of unknown dynamics and the induced disturbances. In the case of the online tuned PD controller, it attains an improved tracking performance under the presence of unknown dynamics and disturbances due to its adaptive characteristics; however, the actual trajectory is still affected by oscillations around the reference signal. In contrast, the developed DNCS enables the system to track satisfactorily the reference signal, with a significantly less tracking error compared with the other controllers. The estimation of the lumped disturbances presented in Figures 3b and 4b by using the dynamic neural network enables the DNCS to increase the performance of the underwater vehicle.  
From Figures 2(a) and 2(a), we can observe that the parameters \(\hat{b}_{\psi}(t)\) and \(\hat{b}_{z}(t)\) converge to a small region in about 4 and 12 s, respectively. Additionally, we observe that the smaller the identification error of the parameters \(\hat{b}_{\psi}(t)\) and \(\hat{b}_{z}(t)\), the better approximation of the unknown dynamics \(\hat{f}_{\psi}\) and \(\hat{f}_{z}\) obtained. However, even when the adaptive parameters are far from their true value, the compensation of the dynamic neural network approximation enables us to conduct satisfactory trajectory tracking in the first seconds of the simulation, as can be observed in Figures 2(a) and 2(a).
## 4 Case Study
### Characterization of the Fuzzy Logic System  
Many construction site scenarios and their level of occupational risks can be evaluated by employing the designed IOT-JFML system. To do that, first, the FLS must be defined where expert knowledge on FFH is considered in order to select variables to support the problem at hand.  
Then, with the aim to clarify the definition of an FLS for FFH, where the OHS legislation, expert knowledge on FFH, input variables, their association to gradual concepts by means of fuzzy logic, etc., a methodology was defined. Based on the information gathered, tentative rules are proposed and reviewed by a panel of experts in this field of knowledge (eight persons with expert knowledge on FFH). If the experts do not validate the rules, the process is initiated in order to propose new variables and rules; however, if the proposal is validated, a fuzzy logic system is defined. Figure 3 illustrates the procedure to define the FLS and to model it according to the IEEE std 1855-2016.  
Notice that the aim of the IEEE std 1855-2016 for Fuzzy Markup Language is to provide the research community a human-readable and hardware independent way to represent an FLS. This standard includes several fuzzification methods and inference systems. In this paper, since the objective is to propose a distributed approach that collects sensor data and automatically and intelligently acts by considering expert knowledge in FFH, we use Mamdani as an inference method and COA as a defuzzification method since they are the most widely used in this type of problem [58,66]. In the same way, we use trapezoid membership functions to model fuzzy terms due to they model fuzzy terms adequately in a gradual domain.  
The knowledge base with different fuzzy variables and their fuzzy terms are stated first (Section 4.1.1). Then, considering the expert knowledge and the relations between the  
Figure 3: Definition of the FLS.  
fuzzy variables, the rule base is also defined (Section 4.2). Last, according to the IEEE std 1855-2016 for Fuzzy Markup Language, an FLS representation is described (Section 4.3).
## 3 Results and Discussion
### Pattern Recognition Analysis
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & **Precision** & **Recall** & **f1-Score** & **Support** & **Accuracy** & \multicolumn{3}{c}{**Confusion Matrix**} \\ \cline{2-10}  & & & Model & & DNT & NONE & RDX & TNT \\ \hline
**DNT** & 0.997 & 0.997 & 0.997 & 347 & 0.997 & 346 & 1 & 0 & 0 \\
**NONE** & 1.000 & 0.996 & 0.998 & 564 & & 0 & 564 & 0 & 0 \\
**RDX** & 0.993 & 1.000 & 0.997 & 149 & & 1 & 0 & 148 & 0 \\
**TNT** & 0.990 & 1.000 & 0.995 & 100 & & 0 & 1 & 0 & 99 \\ \hline  & & & Test & & DNT & NONE & RDX & TNT \\ \hline
**DNT** & 1.000 & 0.997 & 0.998 & 289 & 0.996 & 289 & 0 & 0 & 0 \\
**NONE** & 0.993 & 1.000 & 0.997 & 433 & & 0 & 430 & 1 & 2 \\
**RDX** & 0.993 & 0.993 & 0.993 & 139 & & 1 & 0 & 138 & 0 \\
**TNT** & 1.000 & 0.981 & 0.990 & 102 & & 0 & 0 & 0 & 102 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Confusion matrix and evaluation criteria for the random forest classification model. Figure 6: (a) Probability distribution of the sum of \(\beta_{i}\) (SUM (\(\beta\))) for a simple binary discrimination model. (b) Comparison of the tested machine learning (ML) methods in terms of their log-loss and accuracy.
## 2 Materials and Methods
### MODIS and Landsat Images and Vegetation Indices  
The 8-day composite MODIS surface reflectance product (MOD09A1) [35] was used to calculate two commonly used vegetation indices (Vis): NDVI, (Equation (1)) [17; 18] and EVI (Equation (2)) [22]. Both NDVI and EVI were then used to derive vegetation phenology metrics and greenness.  
\[\mathrm{NDVI}=\frac{\rho_{\mathrm{nir}}-\rho_{\mathrm{red}}}{\rho_{\mathrm{nir }}+\rho_{\mathrm{red}}} \tag{1}\]  
\[\mathrm{EVI}=2.5\times\frac{\rho_{\mathrm{nir}}-\rho_{\mathrm{red}}}{\rho_{ \mathrm{nir}}+6.0\times\rho_{\mathrm{red}}}-7.5\times\rho_{\mathrm{blue}}+1 \tag{2}\]  
Our study area is located in the center of the image tile (Path28/Row35) and was not affected by the strips in Landsat 7 imagery caused by the failure of the ETM+ scan line corrector. Thus, both Landsat 7 and Landsat 8 data were included in the analysis. All of the available Landsat 7 and Landsat 8 surface reflectance products during the study period (2000-2015) covering the study area were downloaded from the USGS EarthExplorer ([[http://earthexplorer.usgs.gov/](http://earthexplorer.usgs.gov/)]([http://earthexplorer.usgs.gov/](http://earthexplorer.usgs.gov/)), accessed on 10 July 2020) and images for the NP and IP were extracted. Only clear pixels were included in data analysis based on the quality assessment band, which helped to exclude image artifacts caused by cloud, cloud shadow, and aerosols [37; 53]. The color composite images (shortwave infrared (SWIR-2), near infrared (NIR), and red bands as RGB) were used to reduce atmospheric effects and to highlight vegetation. The Landsat EVI values for the paired and homogeneous subplots were calculated to compare the impacts of management practices on vegetation phenology.
## 5 Scheme of Hand Gesture Recognition  
### Convert Motion Signal to Time-Frequency Response  
After judging the start time of the hand gesture, we need to evaluate the acquired signal further for hand gesture recognition. We proposed a method of utilizing the time-frequency response to evaluate gestures. Figure 7 shows the block diagram of the proposed method. As the common gesture motions were performed for approximately 1 s, we could pick only the signal data in this time interval for performing the analysis of the time-frequency response once the start of the hand gesture was judged. This effectively reduced the electrical power of the microprocessor required for implementing this technique on  
Figure 6: (**a**) The frequencies of the maximum peaks under a push hand gesture motion. (**b**) The amplitudes of the maximum peaks analyzed with the signal as (**a**). (**c**) The signal in (**b**) after 2 nd differentiation. (**d**) The result of \(T_{pick}\left[n\right]\) used to judge the start time of hand gesture.  
a portable device. In this investigation, we took 150,000 sample points, which corresponded to a signal of a duration of 3 s, for spectrogram analysis while monitoring the occurrence of hand gestures. More specifically, once we detected the data point (=i) representing the start time of the hand gesture, the data points ranging from (i \(-\) 69,999) to (i + 80,000) were extracted for the spectrogram analysis. Considering the data length of 150,000 was a conservative choice to analyze a hand gesture motion fully because the resulting spectrogram, which is described in the following section, indicated that an effective hand gesture pattern only occupied approximately one half of the spectrogram in the x direction (time axis). The data length for judging one gesture could be significantly reduced by employing this technique in future real-time applications.  
The analysis of the time-frequency response utilized the short-time Fourier transform (STFT). The two STFT parameters important for our analysis are the window and overlap sizes. The window size affected the resolution in the time-domain variation. Although a small window size yielded a better time resolution, the frequency-domain resolution was poor. The overlap size was also critical for the resolution in both the time and frequency domains. A smaller overlapping resulted in a worse resolution, which indicated that the pixelated image appeared in the spectrogram. However, a smoother spectrogram with a larger overlapping required a considerable calculation effort. A Hamming window with a window size of 20,000 and an overlap size of 19,000 was selected in this study. This indicates that the spectrogram derived for our hand gesture recognition had a time resolution of 0.02 s and a frequency resolution of 2.5 Hz.
###### Abstract  
This study aims to explore the impact of different fibers on the explosive spalling resistance, residual compressive strength, residual flexural tensile strength, and mass loss of self-compacting concrete (SCC) after heating to elevated temperatures. The fiber types, fiber contents, and target temperatures are selected as the tested variables. Steel fibers (SF), polypropylene fibers (PF), and their combination are used to improve the free resistance of SCC. Then, the specimens are tested after exposure to different target temperatures varying from 200 \({}^{\circ}\)C to 1000 \({}^{\circ}\)C and cooling down to room temperature naturally. The obtained results reveal that PF can effectively prevent explosive spalling in the SCC. Moreover, it was found that the temperature of 400 \({}^{\circ}\)C is a critical temperature for the residual compressive strength of fiber-reinforced SCC. Regarding the flexural tensile strength, the temperature of 200 \({}^{\circ}\)C was found to be a changing point of the SCC after heating to elevated temperatures. A sharp decrease in the compressive strength is observed when the target temperature exceeds 400 \({}^{\circ}\)C. For temperatures below 400 \({}^{\circ}\)C, the compressive strength is enhanced slightly with the increase of the target temperature. Moreover, incorporating a fiber cocktail is an effective way to improve the explosive spalling resistance and residual mechanical properties in the case of fire. Finally, the regression analysis was performed and the proposed correlations between the residual strengths and elevated temperatures are compared with equations in the literature. Good accuracy of the proposed correlation is achieved for fiber-reinforced SCC.  
Keywords:fibers; self-compacting concrete; residual mechanical properties; high temperature; explosive spalling +
Footnote †: journal: Applied Sciences
## 5 Computer Simulation
### Transmission Period for IPT  
When IPT is activated, the core node transmits owing packets according to the predetermined transmission period as summarized in Table 1. In this paper, it is manually determined while considering intra/inter-path interference. For instance, in the case that the destination node locates in 1 hop, intra/inter-path interference is improbable and thus the next buffered packet can be sent out in a transmission period of 0. Maximum hop count in the evaluation topology is \(M_{i}=4\) and the number of adjacent nodes of the core node is also \(N=4\). It sufficiently satisfies the condition in (4) which ensures an efficient packet forwarding by RR-IPT. When RR-Bi-IPT is applied for Ch.2, we set a transmission period of 700 \(\upmu\)s since the 9 th node also transmits uplink packets and it causes inter-path interference with the 2 nd node.
## 3 Validation
### Probabilistic Numerical Model
#### 3.2.2 Estimation of the Model Parameters  
To ensure a consistent application of the model, it is crucial to precisely determine the parameters governing both the Weibull and lognormal distributions. These distributions  
Figure 2: An overview of the formulation of the 3D probabilistic macroscopic model for “semi-explicit” cracking of concrete is provided in this figure. (**a**) illustrates the material heterogeneity, while (**b**) shows the correlation between the degree of heterogeneity, volume effects, and the utilization of random mechanical properties distribution. (**c**) presents the random distributions and the elementary behavior of energy dissipation during damage evolution. Finally, (**d**) demonstrates an example of the global behavior obtained using the Monte Carlo method.  
typically involve two parameters each. However, considering fracture energy as an intrinsic material property implies a constant mean value. Consequently, once its mean value is known, the task entails determining the scale and shape parameters of the Weibull distribution and the standard deviation of the lognormal distribution.  
The assessment of Weibull distribution parameters involves an iterative numerical procedure designed to solve a nonlinear system of equations. This system merges equations that define the distribution's mean and standard deviation, described in Equations (10) and (14), with the analytical scale law introduced by [21], described in Equations (4)-(7). This scale law estimates the expected mean and standard deviation values for a specified concrete volume; here, it is applied to the finite element scale. This formulation originates from an experimental investigation intended to establish a relationship between concrete heterogeneity and the phenomenon of the scale effect. Through this procedure, each finite element received specific parameters \((b,c)\) defining the Weibull distribution that characterizes its behavior. Additional information about the analytical expressions and the implementation of the iterative procedure can be found in [12]. Conversely, the methodology outlining the estimation of standard deviation for the lognormal distribution is detailed in Section 2, while the approach to estimate its mean value is described in Section 4.