esa-sceva/satcom-chunk-collection huggingface dataset:
# Machine Learning-Based Precursor Detection Using Seismic Multi-Parameter Data
###### Abstract  
The application of certain mathematical-statistical methods can quantitatively identify and extract the abnormal characteristics from the observation data, and the comprehensive analysis of seismic multi-parameters can study and judge the risk of the tectonic regions better than a single parameter. In this study, the machine learning-based detection of seismic multi-parameters using the sliding extreme value relevancy method, based on the earthquake-corresponding relevancy spectrum, was calculated in the tectonic regions in the western Chinese mainland, and the \(R\)-value evaluation was completed. Multi-parameter data included the \(b\) value, \(M\) value (missing earthquakes), \(\eta\) value (the relationship between seismic magnitude and frequency), \(D\) value (seismic hazard), \(M\bar{y}\) value (intensity factor), \(N\) value (earthquake frequency), and \(Rm\) value (modulation parameter). The temporal results showed that the high-value anomalies appeared before most target earthquakes during the training period. Moreover, some target earthquakes also occurred during the advantageous extrapolation period with high-value anomalies. The spatial results showed that some months before the target earthquakes, there was indeed a significant abnormal enhancement area that appeared near the epicenter, and the anomaly gradually disappeared after the earthquakes. This study demonstrated that machine learning techniques for detecting earthquake anomalies using seismic multi-parameter data were feasible.  
machine learning; precursor detection; seismic multi-parameter; target earthquake +
Footnote †: journal: Applied Sciences
## 3 Analysis and Results
### Demultiplexer Formed by the MIM Waveguide with Multiple Output Channels
By numerical simulation of the field distribution in the demultiplexer, the spectra of the three output channels are obtained and shown in Figure 7. With the bias electric field applied, the LC with changed refractive index in the channels in the MIM waveguide shifts the resonant frequencies of all output channels obviously. Figure 7 shows the effects of the bias electric field on the spectra of the output channels. In Figure 7a, for example, the resonant frequency of channel 1 is successfully shifted from 0.7315 THz to 0.6414 THz when the bias electric field changes from 0 kV/m to 7 kV/m. The elaborated effects of the bias electric field of the LC on the spectra of the output channels are listed in Table 1. It can be concluded that the larger bias electric field of the LC shifts the resonant frequencies of all output channels to the lower resonant frequencies, resulting in a tunable demultiplexer. This is because, when a higher bias electric field is applied, the effective refractive index, \(n_{\textit{eff}}\), of the MIM waveguide is larger, which can be deduced from \(k_{z}=\omega/c\ n_{\textit{eff}}\) and Figure 3. Consequently, the resonant frequencies governed by Equation (8) can be shifted to lower values due to the larger \(n_{\textit{eff}}\). It should be noted that, although the dimensions of the main and output channels of the demultiplexer are well below the wavelength of the terahertz wave, a fraction of signals can still be transmitted and demultiplexed. Previous research on the LC-infiltrated FP resonator-based filter experimentally showed that around 20% of terahertz signals could be filtered and outputted, where the channel dimension was well below the operating wavelength [40]. Figure 6: Schematic of the LC-infiltrated demultiplexer tuned by the bias voltage. Figure 8 shows the \(\left|H_{y}\right|\) distributions of the output channels as well as the main channel in dB scale. For the sake of intuitive and clarity, the intensity of \(\left|H_{y}\right|\) field in Figure 8 is represented by height as well as color. The demultiplexing effect can be observed from Figure 8. Taking channel 1 at the \(z\) coordinate of 179 \(\upmu\)m as an example, the field amplitude in the output channel 1 is higher at 0.7315 THz, as shown in Figure 8a, compared to those at 1.068 THz and 1.429 THz, as shown in Figure 8b,c respectively. Other output channels can also have a peak amplitude of fields at its corresponding resonant frequencies, thus rendering a good demultiplexing effect. Because different bias electric fields of the LC still lead to the identical peak location of the \(m\)-th resonant mode, Figure 8 just reflects the field distribution with zero-biased electric field of the LC. It should be noted that the absolute amplitude of \(H_{y}\) field at 1.068 THz is relatively smaller than those at other frequencies in Figure 8. This is because the actual field response of the MIM waveguide at different frequencies may vary.
# Using Optimisation Meta-Heuristics for the Roughness Estimation Problem in River Flow Analysis
## 1 Introduction
These works highlight the presence of a strong sensitivity of the empirical method for evaluating the parameters, which seems to highly depend also on the collection of suitable data. Several studies [11; 24] have shown that a well-calibrated flood inundation model may perform very poorly when it is used to predict events different from those used for the calibration. Challenges one may encounter while trying to estimate \(n\) are due to its dynamic nature. In an open channel flow context, this value is subject to variations and can significantly change over time and space. Factors responsible for such changes can be quite varied, including geometric and geomorphological factors as well as others related to the parameters characterising water currents and river beds [25], thus adding uncertainty to the estimated value. On top of these difficulties, the average computational time for a hydrodynamic simulation using standard methodologies is of the order of hours depending on the extent of the modelled area and therefore of the computational domain. In this light, with this piece of research, we aim to improve upon this estimation process by proposing an optimisation-based numerical approach to determine the most suitable Manning's coefficients combination and, at the same time, to reduce, at least in a pre-processing phase, the time-consuming approach of the standard numerical solution methods. To consolidate and show the quality of our method, we apply it first to a real-world context based on the flood events of the river Paglia (an Italian river located in Umbria) occurred in 2012 and, in a second analysis, to the case of a left tributary of the Tiber river, the river Aniene. In our study, we test several established meta-heuristics [26] for optimisation to identify the most effective ones for the specific application scenarios. Details on how to reproduce our procedures are given in the remainder of this article, which is structured as follows. * Section 2 introduces the designed objective function which allows formulating the coefficient estimation as an optimisation problem;
* Section 3 describes the hydrodynamic computational model which is used as a black-box component inside the designed objective function;
* Section 4 briefly recalls the five meta-heuristic considered in this work to optimise the proposed objective function;
* Section 5 presents two case studies and describes the set-up of the conducted experiments;
* Section 6 analyses the experimental results;
* Finally, conclusions are drawn in Section 7 where future lines of research are also depicted.
## 3 Flexible Substrate Antenna Design  
The design procedure for the development of the flexible substrate antennas, illustrated in Figure 3, is comprised of five major steps. First, the preferred conductive and substrate materials are selected according to the required application, in this case for a wearable flexible antenna. The selection of the substrate material for wearable applications is generally challenging because of the necessity for compatibility with the human body, and the need to be able to withstand twisting and bending without distorting its radiation characteristics. The second step is to investigate the electrical, mechanical, and thermal properties of the proposed dielectric flexible substrate in terms of its conductivity, resistivity of the conductive material and loss tangent, permittivity, dielectric strength, tensile modulus, CLTE, density, and moisture absorption, because a suitable flexible substrate needs to possess a high level of deformability, and thermal and electrical stability, with low fabrication complexity. Once the conductive and the substrate material are selected for the antenna, the next step is to design the antenna. This includes the designation of a geometrical shape appropriate for the required application for its frequency requirement, its mathematical modelling, which includes a dimensional and parametric analysis, and the simulation and optimization of the design. In the fourth step, an appropriate fabrication technique, based on the materials, is selected for the reproduction of the design on the  
Figure 2: Antenna Equivalent Model for bent conditions, with circuit parameters Ye, Ys, Ycm (**a**) flat antenna, (**b**) Level 1 presents a minor radial bending, (**c**) Level 2 is higher, and (**d**) Level n is the highest level of curvature.  
flexible substrates. Finally, the fabricated flexible antenna is tested for various radiation characteristics such as reflection coefficients, radiation patterns, gain, and directivities of the antenna. Further qualitative tests for wearable applications, irrelevant to conventional antennas on rigid substrates, such as bending impact on \(S\)-parameters, durability, robustness, moisture, and thermal tests, are also necessary.  
Significant research has been conducted on polymers in recent decades for their application in flexible antennas, due to their advantages of high flexibility, convenient integration with other microwave components [6], light weight, energy efficiency, reduced fabrication complexity, easy mountability on conformal surfaces, low cost, and abundant availability in the form of substrate films [7]. In this study, attention was dedicated to the design and investigation of flexible polymer-based wearable antennas. The different steps for the flexible polymer-based antenna design procedure are described below.
# Towards Crowdsourcing Internet of Things (Crowd-IoT): Architectures, Security and Applications
## 3 Architectures of Crowdsourcing and Proposed Crowdsourcing Internet of Things
### Decentralized Architectures of Crowdsourcing  
Fog computing for a decentralized computing structure has become more important and can offer benefits such as wide geographical distribution, low response delay, and location awareness. In the fog-based mobile crowdsourcing architecture, caching/forwarding nodes are utilized between the platform and users. The fog layer can be added to give low latency service for IoT. The addition of this layer not only offers fog computing advantages. For tasks with spatial-temporal requirements, the distribution of fog node locations can provide a precondition for task forwarding and improve task allocation efficiency. It also helps to prevent the crowdsourcing platforms from eavesdropping on users' privacy. The previous subsection presented the generalized or centralized crowdsourcing architectures, whereas this subsection focuses on the decentralized crowdsourcing architectures, including fog architecture. Table 3 summarizes the research works for decentralized architectures, including fog architectures. Figure 3: Crowdsourcing in the social Internet of things [23]. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline
**Authors \& Ref** & **Year** & **Focus Areas** \\ \hline \hline [PERSON] et al. [24] & 2013 & Proposed a distributed crowdsourcing system in Delay \\ \hline [PERSON] and [PERSON] [25] & 2013 & The monitoring quality function was defined and applied to find the spatial-temporal correlation between monitoring nodes. \\ \hline [PERSON] and [PERSON] [26] & 2014 & Proposed topology repair algorithm based on redundant node selection for the problem of shortening network life due to excessive energy consumption of individual nodes. \\ \hline \end{tabular}
\end{table} 
Table 3: Summary of the research works on decentralized architectures of crowdsourcing. The authors in [22] proposed a generic architecture of decentralized architecture of mobile crowdsourcing applications. The architecture is illustrated in Figure 4. The architecture is similar to a mobile peer-to-peer network where each peer can communicate with other peers in the local vicinity and contribute to identifying mobility patterns. In the crowdsourcing process, mobile devices explicitly or implicitly capture sensor data in the background. The decentralized architecture performs the information processing and storage in the mobile device and local database. The authors in [38] proposed a fog-based spatial-temporal crowdsourcing architecture shown in Figure 5. The proposed architecture has three layers: (1) Crowdsourcing platform layer; (2) Fog layer; and (3) User layer. The fog nodes in the fog layer are network components closer to the user end, for example, base stations (BSs), roadside units (RSUs), and edge routers. The functions of fog nodes include computation and storage space. Each node can decide its interest attributes based on the behavior of the mobile users within its vicinity to achieve task matching.
## 5 Conclusions  
This paper suggests a new infrastructure for high-precision real-time positioning on the Moon, called the Lunar Regional Navigation Transceiver System. It is a ground system which transmits navigation messages similar to GNSS. The system itself is autonomous and capable of self-calibrating. It consists of a set of transceivers placed around the Lunar region where excavation or construction works are performed.  
A case study presented here is implemented on the Shackleton Crater, which is a possible candidate for the first Lunar exploration works. A proof of a concept includes 12 different configuration setups for the transceivers' locations along the crater. All configurations were tested against accuracy, signal coverage (inter-visibility), and efficiency, in the sense of the number of the LRNTS nodes. Among 12 tested models, the setup consisting of nine nodes showed the best numerical figures, with horizontal accuracy of 6.7 mm and vertical accuracy of 11.5 mm and availability over 95.5% of the Shackleton area. If deployed in that way, such a reference frame allows centimeter-level real-time positioning of the user receivers throughout the crater. However, if millimeter-level accuracy is needed, local high-precision control networks can be developed around the desired construction spots in the same manner as in terrestrial applications. Then, the local control networks would be tied to the LRNTS coordinate system, including the points surveyed from the local network.  
The results presented here can be replicated for other solar system bodies (preferably Mars) because the suggested methodology does not need permanent communication with the Earth, which makes the concept self-adjustable and self-sustainable. Practical implementation of this research could help all further missions aimed at Moon exploration, which will benefit from having a tested reliable system for precise positioning purposes during all construction and excavation works.  
**Author Contributions:** Conceptualization, D.I.S.; methodology, D.I.S. and V.O.; software, V.O.; validation, J.R., V.P.-P. and G.V.; formal analysis, V.O.; investigation, D.I.S.; resources, D.I.S.; data curation, D.I.S. and V.O.; writing--original draft preparation, D.I.S. and V.O.; writing--review and editing, J.R., V.P.-P. and G.V.; visualization, D.I.S.; supervision, V.P.-P. and G.V.; project administration, V.P.-P. and G.V. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by research program P2-0268 (ARRS Research and Infrastructure Programs) financed by the Slovenian Research Agency.  
**Data Availability Statement:** All data used in this study are freely available on request to the authors.  
**Acknowledgments:** This research was supported by the Slovenian Ministry of Education Science and Sport, Faculty of Natural Sciences and Engineering.  
**Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analysis, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
## 4 Feasibility of Agro-Waste Utilization in Reinforced Concrete (RC) Elements
### Effects of Agro-Wastes on RC Elements
#### 4.3.5 Performance of Coconut Shell and Banana Fiber-Reinforced Concrete  
Concrete reinforced with steel fibers and CSs, partially replacing coarse aggregates and cement with Class F FA, has shown an increase in ultimate moment capacities by 5-14% during beam tests [26]. Additionally, CS concrete beams exhibited good ductile behavior, with crack widths remaining within acceptable limits. It is also suggested that the fire resistance of such concrete types should be further investigated to assess their viability in ensuring fire safety [73].  
Banana fiber-RC beams demonstrated approximately 25% higher flexural strength compared to unreinforced beams, although the compressive strength of the concrete had a limited effect on the ultimate load capacity. This indicates the potential of banana fibers as a performance-enhancing reinforcement material in RC elements [79; 80; 91].  
Studies on flax fiber-reinforced coconut fiber concrete beams revealed that while coconut fiber content had a limited impact on flexural strength, specimens containing 3% coconut fiber achieved the highest energy absorption of approximately 83.23 J compared to other specimens [85]. Research on steel-RC and flax fiber-reinforced polymer tube-confined coconut fiber concrete beams demonstrated that adding coconut fibers significantly improved ultimate load capacity, deflection, and ductility. These findings highlight the potential of coconut fiber additions to reduce reliance on steel reinforcements [76].  
Steel fibers remain a widely used material for improving the durability of RC structures. However, utilizing AWs in the construction sector as part of a sustainability agenda provides significant benefits for conserving natural resources. It is crucial to conduct more comprehensive research on the standalone effects of AWs in concrete applications to fully understand their potential.
# Adaptive Power-Controlled Depth-Based Routing Protocol for Underwater Wireless Sensor Networks
## 3 The Proposed Protocol
### Channel and Energy Consumption Model  
In order to better understand the working principles, in this section, we will combine relevant references to provide the acoustic channel model and energy model. [PERSON] gave an empirical formula in 1967 [22] to describe the underwater path loss and attenuation, as shown in Equation (1).  
\[A(d,f)=A_{0}d^{k}a(f)^{d} \tag{1}\]  
Here, \(d\) represents distance, \(f\) represents frequency, \(A_{0}\) represents unit-normalizing constant, \(k\) represents spreading factor, and \(a(f)\) is the absorption coefficient. Furthermore, \(10\log a(f)\) is given by [PERSON] [23] in dB/km for \(f\) in kHz, as shown in Equation (2).  
\[10\log a(f)=0.11\frac{f^{2}}{1+f^{2}}+44\frac{f^{2}}{4100+f^{2}}+2.75\times 10^ {-4}f^{2}+0.003,f>0.2\;\mathrm{kHz} \tag{2}\]  
The signal-to-noise ratio (SNR) can be derived according to the passive sonar equation [22], as shown in Equation (3).  
\[\gamma_{b}=SL-TL-NL+DI\geq DT \tag{3}\]  
Here, \(\gamma_{b}\) is the SNR at the receiver and \(DI\) is the directive coefficient. For omnidirectional hydrophones, \(DI\) = 0. NL is the underwater ambient noise, which can be found in [24; 25]. \(DT\) is the minimal SNR required for signal acquisition at the receiver. \(SL\) is the output acoustic source level in \(dB\)_repPa_ at the sender.  
For spherical spreading, the transmitting power \(P_{0}\) at the sender can be obtained from [18].  
\[P_{0}=4\pi\times 0.67\times 10^{-18}\times 10^{(DT+TL+NL-DI)/10} \tag{4}\]  
If a node sends \(n\) data packets and receives \(m\) data packets within time \(t\), then the energy consumed by it during \(t\) can be expressed as:  
\[\begin{array}{l}E\\ =\frac{L}{R2}\sum\limits_{i=1}^{n}P_{tx}(i)+\frac{mL}{Ra}P_{rx}+tP_{idle}\end{array} \tag{5}\]Here, \(E_{tx}\), \(E_{rx}\), and \(E_{idle}\) denote the energy consumption in the transmitting, receiving, and idle state, respectively. \(P_{tx}(i)\) is the transmitting power for the \(i\)-th data packet for the node. \(P_{rx}\) and \(P_{idle}\) are the receiving and idle power in Watts for one node, respectively. We assume that \(P_{rx}\) and \(P_{idle}\) are the same for each node. \(L\) and \(Ra\) represent the packet length and the data rate, measured in bits and bits/s, respectively.
## 2 Methodology
### Evaluation Index  
To evaluate the validity and reliability of PCA-SVR, we conducted a comparison with the SVR machine learning model. Specifically, we employed the average absolute error (MAE), coefficient of determination (\(R^{2}\)), and root-mean-square error (RMSE) as metrics to assess the prediction error of the model. These three indicators, commonly used to evaluate the performance of prediction models, are defined as follows: RMSE provides a measure of the average magnitude of the errors in a set of predictions, MAE gives the average absolute difference between the predicted and actual values, and \(R^{2}\) quantifies the proportion of the variance in the dependent variable that is predictable from the independent variables. These three indicators were defined as follows:  
MAE \[= \frac{1}{n}\sum_{i=1}^{n}|\hat{y_{i}}-y_{i}|,\] (10)  
\[R^{2}=1-\frac{\sum_{i=1}^{n}\left(y_{i}-\hat{y_{i}}\right)^{2}}{\sum_{i=1}^{n} \left(y_{i}-\overline{y_{i}}\right)^{2}}, \tag{11}\]  
\[\text{RMSE}=\sqrt{\frac{1}{n}\sum_{i=1}^{n}\left(y_{i}-\hat{y_{i}}\right)^{2}}, \tag{12}\]where \(\vartheta_{i}\) denotes the predicted value, \(y_{i}\) denotes the measured value, and \(\overline{y}_{i}\) denotes the average value of the measured value.