esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The potential of Global Navigation Satellite Systems-Reflectometry (GNSS-R) techniques to estimate land surface parameters such as soil moisture (SM) is experimentally studied using 2014-2017 global data from the UK TechDemoSat-1 (TDS-1) mission. The approach is based on the analysis of the sensitivity to SM of different observables extracted from the Delay Doppler Maps (DDM) computed by the Space GNSS Receiver-Remote Sensing Instrument (SGR-ReSI) instrument using the L1 (1575.42 MHz) left-hand circularly-polarized (LHCP) reflected signals emitted by the Global Positioning System (GPS) navigation satellites. The sensitivity of different GNSS-R observables to SM and its dependence on the incidence angle is analyzed. It is found that the sensitivity of the calibrated GNSS-R reflectivity to surface soil moisture is \(\sim\) 0.09 dB/% up to 30\({}^{\circ}\) incidence angle, and it decreases with increasing incidence angles, although differences are found depending on the spatial scale used for the ground-truth, and the region. The sensitivity to subsurface soil moisture has been also analyzed using a network of subsurface probes and hydrological models, apparently showing some dependence, but so far results are not conclusive.  
soil moisture; GNSS-R; satellite; hydrology; model; probe; sensitivity 1 2018 10.3390/rs10111856 doi:10.  
A sensitivity study of TechDemoSat-1 data to SM over the whole globe is presented over the period September 2014 to July 2017. This study is organized as follows. In Section 2 the data used are described: the TDS-1 observables, the ESA SMOS (Soil Moisture and Ocean Salinity) mission L3 and L4 products, the in-situ data from the CEMADEM (Centro Nacional de Monitoramento e Alertas de Desastres Naturais) network in Brazil, and two hydrological models to estimate the subsurface soil moisture (LISFLOOD and MUSA) are presented. Section 3 presents the results and discussion of the TDS-1 data analysis on a global scale using SMOS L3 soil moisture gridded at 25 km, and over the three regional networks at two different spatial resolutions using SMOS L3 products at 25 km, and a downscaled SMOS L4 product at 1 km. Due to the limited number of collocations, results using in situ probes are also presented over the Brazilian \"semi-arid\" region. Finally, an attempt is made to estimate the sensitivity to subsurface soil moisture using the data from the two hydrological models (LISFLOOD and MUSA) run over the CEMADEM network, although results are not conclusive. Sections 4 and 5 present the overall discussion of the results and the conclusions of this study.
## 2 Development of the Pre-Tuning Machine
### Resonant Frequency and Field Distribution Testing  
The measurement system is a key part of the pre-tuning machine. It contains a network analyzer, a stepper motor, a small bead-pull system, and control software. The axial electric field flatness is measured using the bead-pull method based on perturbation theory [12; 13]. As shown in Figure 3, a thin line runs through the cavity center. The weight is attached to one end for tension while the other end connects to a motorized wheel. A small metal bead is attached to the line, and as the stepper motor rotates the wheel, the bead moves along the axis of the cavity, perturbing its electromagnetic field. Probes at both cavities end to measure the S21 parameter via a vector network analyzer. Analyzing the correlation between the resonant frequency shift and the bead's position enables the determination of the axial electric field distribution [14].  
Figure 3: Principle diagram of the resonant frequency and field flatness-testing system.  
Figure 2: Overall architecture of the pre-tuning machine.  
When the small bead moves inside the resonant cavity, the perturbations it generates at each point are directly related to the electric field \(E\) and magnetic field \(H\) at that location, as described by Equation (1) [15; 16].  
\[\frac{\Delta\omega_{0}}{\omega_{0}}=-\frac{3\Delta V}{4U}\bigg{(}\frac{\varepsilon _{r}-1}{\varepsilon_{r}+2}\varepsilon_{0}E^{2}+\frac{\mu_{r}-1}{\mu_{r}+2}\mu_ {0}H^{2}\bigg{)} \tag{1}\]  
Here, \(U\) is the total stored energy of the resonant cavity, \(\varepsilon_{r}\) and \(\mu_{r}\) represent the relative permittivity and permeability of the sphere relative to the vacuum, and \(\Delta V\) denotes the volume of the sphere. Since the magnetic field at the central axis of the cavity vanishes \((H=0)\), the equation simplifies to  
\[\frac{\Delta\omega_{0}}{\omega_{0}}=-\frac{3\Delta V}{4U}\frac{\varepsilon_{r }-1}{\varepsilon_{r}+2}\varepsilon_{0}E^{2} \tag{2}\]  
The measurements were conducted using the phase comparison method, where the relationship between frequency and phase is expressed as  
\[\frac{\Delta\omega_{0}}{\omega_{0}}=-\frac{\tan(\Delta\varphi)}{2\text{Q}_{ \text{L}}} \tag{3}\]  
where Q\({}_{\text{L}}\) is the loaded quality factor of the cavity, and \(\Delta\varphi\) denotes the phase shift.  
The electric field flatness can then be calculated using the following formula [17]:  
\[\text{Flatness[\%]}=\left[1-\frac{E_{\text{max}}-E_{\text{min}}}{\frac{1}{N} \sum E_{i}}\right] \tag{4}\]  
where \(E_{\text{max}}\) and \(E_{\text{min}}\) represent the maximum and minimum electric field values, and \(E_{i}\) corresponds to the peak electric field distribution across all unit cells.  
To achieve automation and high precision throughout the pre-tuning process, the data acquisition and control system plays an important role. This system collects resonant frequency and electric field data from the network analyzer in real time through an operating system and data acquisition module and calculates the tuning amount for each unit using a control module.
## 1 Introduction
This makes the substrate current an important measure of HCI severity, while it can no longer accurately reflect HCI degradation due to other factors' influences, such as the gate leakage current and junction current at nanoscale nodes [13]. As the single power-law relationship cannot explain the saturation of HCI degradation, which has been reported, a self-saturation power-law framework is proposed. [PERSON] et al. developed a compact model based on this self-saturating power-law framework in their work [14]. Regarding the mechanism of trap generation, the two-stage effect observed in HCI degradation in FinFETs indicates that different types of traps are generated, making it essential to understand their time/voltage dependence [15]. In addition, it has been reported that substrate voltage may be helpful in improving HCI performance [6; 16], and the effect of fin width as well as the number of fins on HCI has also been investigated [14; 16]. It is essential to include these factors in modeling considerations. This article develops a compact model of HCI degradation based on reaction-diffusion (R-D) theory to account for coupled self-heating effects (SHE) across different \(V_{\rm gs}/V_{\rm ds}\) spans. It integrates the impact on device structural parameters, temperature, and substrate potential, on the basis of the results in [17], and is validated for n-type Bulk FinFETs at the 14 nm node. The main sections of the article are as follows: In the first section, the background of HCI modeling and the previous models are introduced. The second section describes the physical mechanisms, including the reaction-diffusion theory, multivibrational excitation theory, and theory of bond dissociation. On basis of the physical theory, we establish a new compact model. The third section extracts relevant aging parameters and validates the results. We also compare the prediction of the compact model with the TCAD simulation results, and we analyze the life prediction made by the compact model and traditional self-saturating model.
# Intelligent Fault Diagnosis of Rotating Machinery Using Hierarchical Lempel-Ziv Complexity
## 4 Experimental Validations  
In order to validate the effectiveness of the proposed HLZC method in extracting the fault features, we designed two experiments in this paper. The two experiments were conducted on a fault simulator made by SpectraQuest called machinery fault simulator (MFS), which is drawn in Figure 10. The MFS consists of rolling bearings, a driven motor, and a three-way gearbox. At the rear of the gearbox, a magnetic clutch was used to generate the radial load. To collect the vibration data, we installed an acceleration transducer on the top of the gearbox. The rotating speed of the motor was kept at constant vale of 3000 rpm. Note that a 5 in-lbs of torque was added to simulate the machine load environment. The sampling frequency was 12,800 Hz. To simulate different faults, we replaced the test bearing and test gear by artificial damaged gear and artificial local damaged bearing, as shown in Figure 11.  
Experiment 1 aimed to show the superiority of the proposed HLZC method for single FDRM, which only covers single fault types of rotating machinery. Experiment 2 aimed to simulate the compound fault of rotating machinery including the bearing and gear fault, which was used to validate the superiority of the proposed HLZC method for compound FDRM. In this experiment, 50% of samples were randomly chosen as training samples, and the remainder of samples were used as testing samples. For comparison purposes, LZC and MLZC were all applied to process the data collected from the two experiments.
# Intrusion Detection and Prevention in CoAP Wireless Sensor Networks Using Anomaly Detection
## 3 Anomaly-Based Intrusion Detection in Internet-Integrated CoAP WSN
### Considered Approach  
The approach considered in our work may be characterized along the taxonomy proposed in [12], as is characterized as follows:  
* System network architecture: we adopt a centralized approach, with the goal of detecting attacks subverting the client-server model of CoAP in the range of the system detecting communications.
* Networking type: as is common in sensor network communication environments, a wireless hierarchical model is considered, with IEEE 802.15.4 at the link layer (supporting hop-by-hop communications), and 6 LoWPAN, RPL and CoAP at the higher layers.
* Collection component: For traffic collection, we use an agent, in particular an IoT-LAB sniffer node capable of capturing the traffic in a specific location of the network.
* Data collection: data collection is centralized, given that the capture of the network traffic is performed in a specific node of the network.
* Data type: We store the captured wireless network traffic in the pcap (Packet Capture) format.
* Time of detection: in our implementation it is currently performed off-line.
* Granularity: in our current implementation we adopt a periodic (batch) approach to traffic capturing.
* Detection discipline: we consider the state-based and stimulating evaluation disciplines, since the IDS reports if a node is in the normal or compromised state. We identify the former as \"NORMAL\" throughout the article, and the latter as \"INTRUSION\".
* Processing strategy: we adopt a centralized approach to processing the gathered data and to detect attacks.
* Detection methodology: as previously discussed, we currently consider, implement and evaluate anomaly-based CoAP intrusion detection in the context of the proposed framework.  
We proceed by analyzing the proposed framework for intrusion detection and prevention in the context of Internet-integrated CoAP sensor networks.
# Nascent Rice Husk as an Adsorbent for Removing Cationic Dyes from Textile Wastewater
## 4 Conclusions  
RH, an agricultural byproduct, was utilized as an affordable resource to remove MB and CV from an aqueous solution. Pyrolysis of the RH above 300 \({}^{\circ}\)C was ineffective in enhancing its adsorption capacity for MB and it was opposite to the removal of dyes by the adsorbents derived from biomass. RH treated at 75 \({}^{\circ}\)C, had a superior removal capacity than RH at other temperatures (100 \({}^{\circ}\)C, 300 \({}^{\circ}\)C, 500 \({}^{\circ}\)C, and 700 \({}^{\circ}\)C). The kinetic adsorption for MB and CV was well represented by the PFO model and PSO model, respectively. The adsorption rate of MB by RH was mainly dominated by film-diffusion or intra-particle diffusion, but the adsorption for CV was by chemisorption. The equilibrium adsorption of both MB and CV by RH were well-fitted to the Langmuir model, revealing a homogenous monolayer coverage of MB and CV on the outer surface of the RH. The adsorption of MB and CV onto RH was endothermic and spontaneous. Solution pH barely influenced the adsorption of both, MB and CV onto RH. An increasing RH dosage led to an increase in the removal percentage and a decrease in the adsorbed dyes per unit mass of RH. The adsorption capacity of RH for both, MB and CV was lower than that of other adsorbents in the literature; however, it can be a good candidate for the removal of MB and CV from textile wastewater due to its low price and availability.  
Figure 7: Effect of rice husk (RH) dosage on (**a**) methylene blue (MB) adsorption and (**b**) crystal violet (CV) adsorption  
**Author Contributions:** J.O.Q.: Visualization, Writing--original draft; T.H.: Experiment, Data curation; F.N.L.: Writing--review and editing: P.P.K.: Experiment, Data curation; S.-H.H.: Conceptualization; C.-G.L.: Investigation, Validation; S.-J.P.: Conceptualization, Writing--review and editing, Supervision, Funding acquisition. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by Korea Institute of Planning and Evaluation for Technology in Food, Agriculture, Forestry and Fisheries (IPET) through Advanced Production Technology Development Program, funded by Ministry of Agriculture, Food and Rural Affairs (MAFRA) (Grant No. 317017-03).  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Classification of Virtual Harassment on Social Networks Using Ensemble Learning Techniques
###### Abstract  
Background: Internet social media platforms have become quite popular, enabling a wide range of online users to stay in touch with their friends and relatives wherever they are at any time. This has led to a significant increase in virtual crime from the inception of these platforms to the present day. Users are harassed online when confidential information about them is stolen, or when another user posts insulting or offensive comments about them. This has posed a significant threat to online social media users, both mentally and psychologically. **Methods:** This research compares traditional classifiers and ensemble learning in classifying virtual harassment in online social media networks by using both models with four different datasets: seven machine learning algorithms (Nave Bayes NB, Decision Tree DT, K Nearest Neighbor KNN, Logistics Regression LR, Neural Network NN, Quadratic Discriminant Analysis QDA, and Support Vector Machine SVM) and four ensemble learning models (Ada Boosting, Gradient Boosting, Random Forest, and Max Voting). Finally, we compared our results using twelve evaluation metrics, namely: Accuracy, Precision, Recall, Fl-measure, Specificity, Matthew's Correlation Coefficient (MCC), Cohen's Kappa Coefficient KAPPA, Area Under Curve (AUC), False Discovery Rate (FDR), False Negative Rate (FNR), False Positive Rate (FPR), and Negative Predictive Value (NPV) were used to show the validity of our algorithms. **Results:** At the end of the experiments, For Dataset \(1\), Logistics Regression had the highest accuracy of 0.6923 for machine learning algorithms, while Max Voting Ensemble had the highest accuracy of 0.7047. For dataset 2, K-Nearest Neighbor, Support Vector Machine, and Logistics Regression all had the same highest accuracy of 0.8769 in the machine learning algorithm, while Random Forest and Gradient Boosting Ensemble both had the highest accuracy of 0.8779. For dataset 3, the Support Vector Machine had the highest accuracy of 0.9243 for the machine learning algorithms, while the Random Forest ensemble had the highest accuracy of 0.9258. For dataset 4, the Support Vector Machine and Logistics Regression both had 0.8383, while the Max voting ensemble obtained an accuracy of 0.8280. A bar chart was used to represent our results, showing the minimum, maximum, and quartile ranges. **Conclusions:** Undoubtedly, this technique has assisted in no small measure in comparing the selected machine learning algorithms as well as the ensemble for detecting and exposing various forms of cyber harassment in cyberspace. Finally, the best and weakest algorithms were revealed.  
harassment; classification; ensemble; metrics; algorithm; learning; classifiers +
Footnote †: journal: Applied Sciences
# Optimization of Pico-eNB Tx Power and the Effects of Picocell Range Expansion in Multiband HetNet
## 3 HetNet Model and Simulation Conditions
### MCS Indexes Incorporating 1024-QAM and Fading Channel Model  
MCS is used as the link adaptation to determine the data rate in the downlink which depends on the channel link quality between pico-eNBs and the UE. The MCS defines the number of useful data bits that can be transmitted, which depends on the channel link quality between eNBs and UE. The channel link quality is estimated based on the channel quality indicator which is reported by the UE using the uplink control channels. In the simulations, 25 types of MCS indexes incorporating 1024-QAM are defined in the downlink as listed in Table 2[30]. Macro-eNBs and pico-eNBs can choose the best MCS index based on the channel link quality. The lowest and highest modulation schemes are QPSK and 1024-QAM, respectively. The MCS index incorporating a high QAM is assigned to the UE with better channel link quality, whereas QPSK is always assigned to the UE with worse channel link quality. Multipath fading channels are formed by a tppd with a specific delay and gain. Table 3 shows the TDL model used in the simulation, which provides Rayleigh fading with a delay spread of 30 ns [45]. The TDL model is used in both the macrocells and picocells.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**MCS Index** & **Modulation** & **Coding Rate** & **Efficiency (bps/Hz)** \\ \hline
1 & & 1/13 & 0.152 \\
2 & & 1/9 & 0.234 \\
3 & & 1/5 & 0.377 \\
4 & & 1/3 & 0.667 \\
5 & QPSK & 2/5 & 0.800 \\
6 & & 1/2 & 1.000 \\
7
## 3 Results
### Volatile Organic Compounds Produced by Bacillus Bacteria and F. oxysporum
#### 3.2.1 Volatile Organic Compounds Produced by _Bacillus_ Bacteria  
The analysis of volatiles emitted by three species of _Bacillus_ bacteria (i.e., _B. amyloliquefaciens_, _B. subtilis_ and _B. thuringiensis_) revealed a total of 46 compounds, of which 41 were strictly identified, 2 were classified as phenolic compounds with the formula C\({}_{9}\)H\({}_{12}\)O, and the remaining 3 were not identified (Table 1). In the volatile phase emitted by _B. amyloliquefaciens_, _B. subtilis_ and _B. thuringiensis_, 30, 32 and 26 compounds were identified, respectively (Table 1). The analysed compositions of volatiles were rich in carbonyl compounds, of which heptan-2-one, acetone and two constitutional isomers of methylhepten-2-one, i.e., 5-methylhepten-2-one and 6-methylhepten-2-one, were found in the highest amount. _B. amyloliquefaciens_ had the highest total content of carbonyl compounds (46.83 \(\pm\) 8.41%). The presence of 3-hydroxybutan-2-one, undecan-2-one, dodecan-5-one and tetradecan-5-one was only found in _B. amyloliquefaciens_, 1-hydroxypropan-2-one only in _B. subtilis_ and 3-methylpentan-2-one and pentadecan-2-one only in _B. thuringiensis_ (Table 1). The composition of volatile compounds emitted by _Bacillus_ bacteria contained pyrazine derivatives, including in the highest amount 2,5-dimethyl-, 2,3,5-trimethyl- and 2-methylpyrazine. 2-Methyl-3-isopropylrazine was detected in _B. subtilis_ and _B. amyloliquefaciens_, while 2,5-dimethyl-3-ethylpyrazine was only detected in cultures of _B. thuringiensis_. _B. thuringiensis_ had the highest total content of pyrazine derivatives (48.98 \(\pm\) 4.37%) (Table 1). Alcohols (e.g., heptan-2-ol) and monoterpenes (e.g., \(\delta\)-3-carene) were also found in the samples. Amides such as acetamide and propanamide were exclusively produced by _B. thuringiensis_. Dimethyltrisulphide was detected in _B. subtilis_ and _B. thuringiensis_, while dimethyldisulphide was only detected in _B. thuringiensis_. The volatile compositions of _B. amyloliquefaciens_ and _B. subtilis_ were also characterised by a content of about one percent of alkanes (Table 1). In addition, selected information on the content of chemical compounds provided in the Table 1 is presented graphically in Figure 4. The graphical form of data presentation allows a more illustrative comparison of the content of individual chemical compounds in the composition of volatile compounds emitted by the three studied _Bacillus_ species.
## 4 Bending Analysis
### Effect of Bending on Resonant Frequencies
Conversely, the PET-based flexible antenna,  
Figure 14: Operating at 4.25 GHz, reflection coefficients of E-shaped flexible polymer substrate antennas on PET, Teflon, and PVC substrates non-bent and with bending: (**a**) 27 mm, (**b**) 14 mm. which was very efficient within the 2.2-2.5 GHz range, is highly impacted by bending in terms of frequency shift while operating at the 4.25 GHz frequency; see Table 3.  
The decrease in the size of the antenna with the increase in operating frequency and impedance mismatching may be a problem because, with higher level bending and higher frequency levels, the dissipation factor and dielectric constant of the polymer substrates may also vary. Keeping all these conditions in mind and by performing extensive electrical property tests, three antennas were designed and tested in the chamber for the higher operating frequency of 7.45 GHz. At the higher frequencies, above 5 GHz, impedance matching and feed line connection is a critical phenomenon that affects performance. However, although examined and successfully attained, these were not included in the investigative focus on bending capabilities for this experimental study. The PVC-based flexible antenna is impacted most by the highest bending level of 14 mm, which yields a 1.97% shift towards the highest component of frequency, whereas the PET-based substrate antenna gives very little deviation from the resonant frequency compared to the flat case. Figure 15: Operating at 7.45 GHz, reflection coefficients of E-shaped flexible polymer substrate antennas on PET, Teflon, and PVC substrates non-bent and with bending: (**a**) 27 mm, (**b**) 14 mm.