esa-sceva/satcom-chunk-collection huggingface dataset:
# An Enhanced Energy Balanced Data Transmission Protocol for Underwater Acoustic Sensor Networks
## 4 EBET and EEBET: Proposed Techniques
### Route Establishment
#### 4.1.2 Eebet
As the data transmission process starts, each node transmits data to its selected relay node till its one energy level drops. When the \(RL\) of receiver node drops, it informs its receiver using \(RLC_{p}\). The sender node again asks for the updated \(RL\) information from its pre-selected neighbor nodes in its routing table and selects a node with high residual energy than itself. The selected node receives data till its \(RL\) drops to next level. In this way all the network nodes consume almost same amount of energy and the network becomes energy balanced. Consider four nodes A, B, C and D, where \(A\in R_{1},B\in R_{2},C\in R_{3}\) and \(D\in R_{4}\). Node \(A\) transmits its data packet to node \(C\) while node \(B\) transmit data to \(D\). Nodes \(A\) and \(B\) transmit same amount of data at almost same distance, therefore, they consume same amount of energy. Node \(C\) receives data of \(A\) and also transmit its own data, than its unit energy level drops before node \(A\) consumes one energy level and informs its sender to reselect its relay node. Meanwhile, node \(C\) only send its own data packets. In this way, the data load is shared among different nodes and the node \(A\) starts transmitting its data to another node having high energy than itself. Therefore, it consumes its energy before another selected relay node drop one Energy Level. As a result, the energy becomes balanced between all Ring Sectors and the network becomes energy balanced. Figure 7: Formation of loops during data transmissions. Besides our contribution towards balanced energy transmissions, both EBET and EEBET pose the challenge of high packet drop rate. Therefore, we present linear programming based model to improve our EBET and EEBET techniques in terms of throughput. Details are given in the upcoming subsection.
# Sensing Hydrogen Seeps in the Subsurface for Natural Hydrogen Exploration
## 3 Results and Discussion
### Hydrogen Seep Detection
The most significant discovery to date has been in Mali, where gas comprising 98% hydrogen was produced from the Bougou-1 well as a pilot program and was used to supply energy to the local village for about 5 years. Soil hydrogen concentrations across a circular feature near the well reached up to 600 ppm, indicating this as a possible level where useful quantities of hydrogen are present [5]. The Coolgardie area field study returned extremely variable concentration of hydrogen in soil gas along a seep profile, from 0 to more than 400 ppm. These variations can indicate different domains in the seep and location of the free gas escaping; however, numerous external parameters, such as the soil composition and humidity, could influence these variations. For instance, some authors recommend the use of a portable drill operating in percussion mode only (no rotation of the drill bit) [29; 30] so as to prevent any production of hydrogen gas through heating/cracking of the organic matter [31; 32; 33] and through \"mechano-radical\" processes linked to the dissociation of silicates in water-saturated rocks [34; 35]. This process is supposed to be the origin of high concentrations of H\({}_{2}\) in soil gas associated with active tectonic faults [36; 37]. Note that in the case of the Gironde study [29], all drilling made with a rotary drill bit always produced hydrogen concentrations of at least 1000 ppm. This observation highlights the importance of the baseline and type of soil drilled. For instance, in the case of the CSIRO Moore study [12], with the rotary drilling method, the authors observed a fluctuation in the values from 0 ppm, outside of the fairy circles, to 96 ppm in the external ring of the fairy circles. The measurement location and range of values was repeated over three field campaigns and drilling of similar soils outside of the zone of interest returned a zero-hydrogen concentration. This shows the importance of baseline studies and tests, including the heating and water saturation of new soil types, or performing systematic soil XRD analysis.
## 3 Results
### Intermediate Products and Degradation Mechanism
\begin{table}
\begin{tabular}{c c c c c c} \hline \multirow{2}{*}{**Sample**} & \multirow{2}{*}{**Peak**} & \multirow{2}{*}{_m/z_ [M + H]\({}^{+}\)} & \multirow{2}{*}{\
\begin{tabular}{c} **Molecular** \\ **Weight** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Molecular** \\ **Formula** \\ \end{tabular} } & \multirow{2}{*}{**Molecular**} & \multirow{2}{*}{**Reference(s)**} \\ \hline DCF & & & & 296 & C\({}_{14}\)H\({}_{11}\)Cl\({}_{2}\)NO\({}_{2}\) & \\ \hline \multirow{4}{*}{Nano + EB, flow} & P1 & 134 & 133 & C\({}_{9}\)H\({}_{11}\)N & [22] \\  & P2 & 260 & 259 & C\({}_{14}\)H\({}_{10}\)CINO\({}_{2}\) & [10; 39] \\  & P3 & 312 & 311 & C\({}_{14}\)H\({}_{11}\)Cl\({}_{2}\)NO\({}_{3}\) & [20; 39] \\  & P4 & 312 & 311 & C\({}_{14}\)H\({}_{11}\)Cl\({}_{2}\)NO\({}_{3}\) & [20; 39] \\  & P5 & 294 & 293 & C\({}_{14}\)H\({}_{9}\)Cl\({}_{2}\)NO\({}_{2}\) & [16] \\ \hline \multirow{4}{*}{EB} & P* & 312 & 311 & C\({}_{14}\)H\({}_{11}\)Cl\({}_{2}\)NO\({}_{3}\) & [18; 20; 39] \\  & P\# & 152 & 151 & C\({}_{8}\)H\({}_{9}\)O\({}_{2}\)N & [22; 39] \\ \cline{1-1}  & PS & 134 & 133 & C\({}_{9}\)H\({}_{11}\)N & [22] \\ \cline{1-1}  & P- & 126 & 125 & C\({}_{6}\)H\({}_{4}\)ClN & This work \\ \cline{1-1}  & P’ & 106 & 105 & C\({}_{7}\)H\({}_{7}\)N & [22] \\ \hline \end{tabular}
\end{table} 
Table 2: DCF and its radiolytic by-products in aqueous solution under EB irradiation (EB) and EB together with nanobubbling in flow system (Nano + EB, flow) (DCF\({}_{0}\) = 10 mg/L). Experimental and calculated isotope profiles are shown in Figure A1. Figure 12: Chromatographic profile of DCF solution under electron-beam radiation after nanobubbling process in flow system, recorded at 280 nm. It is important to mention that irradiation doses can influence the generated degradation products of target compounds. Based on this, the identification of other radiolytic by-products of DCF by [PERSON] and [PERSON] [20], [PERSON] et al. [18] and [PERSON] et al. [22] could be attributed to the different doses applied in each study. On the other hand, it is also evident that the decomposition of DCF can follow different reaction pathways depending on the degradation treatment used, as can be observed using the photo-Fenton method [16], UV-activated peroxymonosulfate [10] or photocatalysis using TiO\({}_{2}\)-based zeolite catalyst [12].
# Remote Monitoring of the Impact of Oil Spills on Vegetation in the Niger Delta, Nigeria
## 2 Materials and Methods
### Methods
#### 2.3.2 Selection of Oil Spill Sites and Determining the Impact of Oil Spills on Vegetation
The NDVI values at each site were used to derive a change in NDVI (ANDVI) time series using the following equation using Equation (2):  
\[\Delta\text{NDVI}=\text{NDVI}_{\text{AS}}\text{}^{1}.\ldots.\ldots.\text{ NDVI}_{\text{AS}}\text{}^{s}-\text{NDVI}_{\text{BS}} \tag{2}\]  
where \(\text{NDVI}_{\text{BS}}\) is the baseline NDVI value before an oil spill occurs, whilst \(\text{NDVI}_{\text{AS}}\text{}^{1}\) and \(\text{NDVI}_{\text{AS}}\text{}^{s}\) are the first and subsequent NDVI values after the occurrence of a spill, respectively. Table 3 shows the spill dates, volumes, and temporal coverage for the \(\Delta\text{NDVI}\) time series for dense, sparse and mangrove vegetation sample spill sites. The oil spill volumes at the selected sites for each vegetation type are 280-1500 bbl for dense vegetation, 228-1500 bbl for sparse vegetation, and 264-2500 bbl for mangrove vegetation. Figure 3: Vegetation sample points in red at the spill site, (**a**) before the spill, (**b**) after the spill, and control site, (**c**) before the spill, and (**d**) after the spill for dense vegetation (**Top left**), sparse vegetation (**Top right**), and the mangrove vegetation (**bottom**). \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sample Points** & **Spill Date** & **Spill Volume (bbl)** & **\(\Delta\)NDVI Coverage** \\ \hline D1 & 1 October 2014 & 280 & 2014 to 2018 \\ D2 & 13 October 2011 & 346 & 2012 to 2018 \\ D3 & 25 June 2014 & 367 & 2014 to 2018 \\ D4 & 14 November 2014 & 367 & 2014 to 2018 \\ D5 & 9 April 2011 & 429 & 2012 to 2017 \\ D6 & 25 April 2010 & 1000 & 2011 to 2018 \\ D7 & 31 October 2011 & 1430 & 2012 to 2018 \\ D8 & 25 April 2010 & 1500 & 2010 to 2018 \\ \hline S1 & 19 January 2014 & 228 & 2013 to 2017 \\ S2 & 17 September 2013 & 235 & 2012 to 2017 \\ S3 & 24 June 2010 & 260 & 2010 to 2017 \\ S4 & 8 August 2008 & 440.3 & 2008 to 2018 \\ S5 & 22 January 2012 & 530 & 2011 to 2018 \\ S6 & 23 December 2010 & 803 & 2010 to 2017 \\ S7 & 25 September 2010 & 1000 & 2010 to 2016 \\ S8 & 5 May 2008 & 1500 & 2008 to 2017 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The volumes of oil spills used to analyse the temporal \(\Delta\)NDVI at the sample of spill sites.
# Extreme Learning Machines to Combat Phase Noise in RoF-OFDM Schemes
###### Abstract  
Radio-over-fiber (RoF) orthogonal frequency division multiplexing (OFDM) systems have been revealed as the solution to support secure, cost-effective, and high-capacity wireless access for the future telecommunication systems. Unfortunately, the bandwidth-distance product in these schemes is mainly limited by phase noise that comes from the laser linewidth, as well as the chromatic fiber dispersion. On the other hand, the single-hidden layer feedforward neural network subject to the extreme learning machine (ELM) algorithm has been widely studied in regression and classification problems for different research fields, because of its good generalization performance and extremely fast learning speed. In this work, ELMs in the real and complex domains for direct-detection OFDM-based RoF schemes are proposed for the first time. These artificial neural networks are based on the use of pilot subcarriers as training samples and data subcarriers as testing samples, and consequently, their learning stages occur in real-time without decreasing the effective transmission rate. Regarding the feasible pilot-assisted equalization method, the effectiveness and simplicity of the ELM algorithm in the complex domain are highlighted by evaluation of a QPSK-OFDM signal over an additive white Gaussian noise channel at diverse laser linewidths and chromatic fiber dispersion effects and taking into account several OFDM symbol periods. Considering diverse relationships between the fiber transmission distance and the radio frequency (for practical design purposes) and the duration of a single OFDM symbol equal to 64 ns, the fully-complex ELM followed by the real ELM outperform the pilot-based correction channel in terms of the system performance tolerance against the signal-to-noise ratio and the laser linewidth.  
Keywords:extreme learning machines; orthogonal frequency division multiplexing; phase noise; radio over fiber systems +
Footnote †: journal: Electronics Letters
###### Abstract  
Food disasters have a huge effect on human life, the economy, and the ecosystem. Quickly extracting the spatial extent of flooding is necessary for disaster analysis and rescue planning. Thus, extensive studies have utilized optical or radar data for the extraction of water distribution and monitoring of flood events. As the quality of detected flood inundation coverage by optical images is degraded by cloud cover, the current data products derived from optical sensors cannot meet the needs of rapid flood-range monitoring. The presented study proposes an adaptive thresholding method for extracting water coverage (AT-EWC) regarding rapid flooding from Sentinel-1 synthetic aperture radar (SAR) data with the assistance of prior information from Landsat data. Our method follows three major steps. First, applying the dynamic surface water extent (DSWE) algorithm to Landsat data acquired from the year 2000 to 2016, the distribution probability of water and non-water is calculated through the Google Earth Engine platform. Then, current water coverage is extracted from Sentinel-1 data. Specifically, the persistent water and non-water datasets are used to automatically determine the type of image histogram. Finally, the inundated areas are calculated by combining the persistent water and non-water datasets and the current water coverage as derived from the above two steps. This approach is fast and fully automated for flood detection. In the classification results from the WeiFang and Ji'An sites, the overall classification accuracy of water and land detection reached 95-97%. Our approach is fully automatic. In particular, the proposed algorithm outperforms the traditional method over small water bodies (inland watersheds with few lakes) and makes up for the low temporal resolution of existing water products.  
flood disasters; Sentinel-1; SAR; thresholding; water probability Article  
An Adaptive Thresholding Approach toward Rapid Flood Coverage Extraction  
1
## 3 Materials and Methods
### Dataset Creation
#### 3.1.6. Data Gathering- Video Annotation  
After the recording process, videos of left and right cameras were saved. These videos were saved in a cloud server (Dropbox) and access were given to the annotators. The annotation process was performed as follow:  
1. A total of three (3) PTs (Physical Therapists) served as annotators.
2. The annotation process was done blindly (each annotator will work independently).
3. The annotation is based on the criteria set in Table 5.
4. Initially, a collection of recordings of proper sitting postures were stored.
5. For the recording of improper sitting postures, a total of thirty (30) minutes per participant was captured. These recordings were normalized in this manner: * Video Pre-processing--cutting the thirty (30) minute video into multiple ten (10) second videos. The first ten (10) seconds of the total video recorded was cut since at that time, the participant was just warming up. For the pre-recorded proper sitting postures, each recording was cut to exactly ten (10) seconds. * Removal of \"Unusable Part\"--removal of \"unusable part\" means the removal of positions aside from seated typing (i.e., standing, no person detected, etc.)
6. After all these necessary steps, annotation took place by getting the dominant sitting posture (proper or improper) in every ten (10) second video. This was done as follows: * From the first (1 st) to ten (10 th) seconds, the annotator will check for themominant posture. * Dominant posture is determined by a greater number of seconds (e.g., six (6) seconds of proper and four (4) seconds of improper, then the dominant posture is PROPER, otherwise IMPROPER). In getting the dominant posture, it does not necessary to read the consecutive seconds.
## 2 Material and Methods
### Biofilm Visualization  
A series of Environmental Scanning Electron Microscopy (ESEM) and epifluorescence microscopy images were taken to provide a visual characterization of electrode modifications and the surface morphologies of the biofilm growing over the electrode. Prior to ESEM imaging, fixation was performed using phosphate buffer (400 mM, pH = 7.4) with 4% glutaraldehyde. The samples were rinsed in phosphate buffer containing saccharose (0.4 M) and then dehydrated by immersion in increasing concentrations of the following solutions: acetone (50%, 70%, 100%), acetone and hexamethyldisilazane (50:50), 100% hexamethyldisilazane (HMDS). The last batch of HMDS was dried until all the moisture was removed.  
Prior to epifluorescence microscopy and in order to eliminate all soluble and solid materials except the attached biofilms, microbial biofilms developed on electrodes were washed with sterile physiological water. Before being left to dry at room temperature, bioelectrodes were colored with 0.03% acridine orange (A6014, Sigma) for 10 min. The biofilms were imaged using a Carl Zeiss Axio Imager-M2 microscope equipped for epifluorescence with an HXP 200 C light source and the Zeiss 09 filter (excitor HP450e 490, reflector FT 10, barrier filter LP520). Images were obtained with a digital camera (Zeiss AxioCamMRm) every 0.5 \(\upmu\)m along the Z-axis and the set of images was processed with the Zen(r) software.
## 2 Numerical Approach
### Xfoil  
XFOIL is a code that can compute the aerodynamic properties of an airfoil such as lift, drag, and moment coefficients based on a high order panel method and integral boundary layer method [31]. Once the two-dimensional shape of an airfoil, Reynolds number, and angle of attack for the simulation have been determined, XFOIL analyzes the flow and computes the pressure on the airfoil surface that can be numerically converted to the lift, drag, and moment coefficients of the airfoil. The reason that XFOIL is utilized to compute these values instead of more advanced computational fluid dynamic (CFD) based on Reynolds-averaged Navier-Stokes equations is because XFOIL enables us to obtain the values very efficiently in terms of computation time. Although XFOIL works well for a low Reynolds number and incompressible flow [32], it can compute the aerodynamic properties of a wind turbine airfoil with a reasonable accuracy under the simulation conditions that are the focus of  
Figure 1: The baseline wind turbine airfoil with the control points of the Bezier curve.  
the current study. From the author's preliminary study with the mesh, which has 181,250 elements, a two-dimensional aerodynamic analysis for a baseline airfoil at \(\text{Re}=10^{6}\) and AOA = \(5^{\circ}\) (which are the design and simulation conditions converging faster than other cases) takes approximately 5 min with an 8-core, 32 GB desktop using OpenFOAM, while XFOIL only needs approximately 12 s under these conditions. Because the total amount of calculations required in the current study is huge, XFOIL is selected as the simulation method.  
Figure 2 shows a comparison of experiment data obtained from [PERSON] and [PERSON] [PERSON] and [PERSON] (2006), XFOIL and CFD data with respect to drag and lift coefficients as a function of the angle of attack (varying from \(-15^{\circ}\) to \(15^{\circ}\)) at \(\text{Re}=10^{6}\). For most angles of attack, this data comparison shows good agreement between the lift and drag coefficients computed from the XFOIL simulation and the ones obtained from a wind turbine airfoil experiment.
## 3 Step Modulation Simulation and Discussion
As stated in the introduction, the semiconductor laser would generate a series of gradually decaying spike pulse sequence before the laser achieves stabilized output attributing to the relaxation oscillation. Therefore, when \(T_{2}=124\) ps, another peak appears on the falling edge of the pulse, which means the terminal time of the electrical pulse is too late to suppress the onset of the second optical spike. To make it clear, the simulation output of \(T_{2}=500\) ps is used as a reference, indicated in Figure 4a by dotted lines. Thus, the conclusion two is: When \(I_{2}\) is certain, the optimal \(T_{2}\) should be equal to the minimum time required to saturate the peak power of the optical pulse, that is, regulating \(T_{2}\) until the output peak power reaches saturation. Figure 5a is the relation between \(I_{2}\) and \(T_{2}\), in which the green curve2 is the result based on conclusion one, and the blue curve1 is the result based on conclusion two. It can be seen clearly that curves1 and 2 are basically coincident. To fit the curve1 and curve2 separately, we design an inverse function expression as follows:  
\[T_{2}=\frac{a}{I_{2}+b}+c \tag{4}\]  
Figure 3: Simulation results of output pulses at different \(I_{2}\), when \(I_{1}=34\) mA, \(T_{1}=0.971\) ns, \(T_{2}=64\) ps: (**a**) Time domain graph; (**b**) the peak power and full width at half maximum. where the units of \(I_{2}\) and \(T_{2}\) are mA and ps. In the experimental data disposal, the coefficient of determination is often used as a criterion of fitting goodness. In the case of \(I_{1}=34\) mA and \(T_{1}=0.971\) ns, calculations showed that the coefficient of determination of the fitting curve to curve1 is 0.99139, and the coefficient of determination for curve2 is 0.99242, when \(a=4328.3\) mA\(\cdot\)ps, \(b=24.6\) mA, \(c=18.3\) ps. Therefore, Equation (4) can be used to characterize the relationship between \(I_{2}\) and \(T_{2}\). Figure 5b shows the variation of the peak power and the FWHM of the output pulse when \(I_{2}\) and \(T_{2}\) satisfy the relationship of Equation (4). According to the fitting curve in Figure 5a, it is directly seen that: To get a short pulse with high peak power, firstly, we can increase \(I_{2}\), and then reduce \(T_{2}\), so that the output pulse is more and sharper at the same peak power, until \(I_{2}\) and \(T_{2}\) satisfy the analogous expression of Equation (4); when \(I_{2}\) increases indefinitely, \(T_{2}\) changes slowly and tends to be stable. At this time, although the peak power is still increasing, the output pulse width changes very little and tends to stabilize too.
# GPRS Sensor Node Battery Life Span Prediction Based on Received Signal Quality: Experimental Study
## 4 Data Analysis and Discussion
### SIM 800L Based Sensor None Life Time Estimation  
#### 4.3.1 Battery Life Calculation  
Even if the RSSI value depends on a lot of factors, when it is measured, we assume that the measured value has been found by taking under consideration its affecting factors. Our data analysis result shows that the average consumption for our sensor node in a particular location depends on the received signal quality at that location; this can be shown on the average currents in Table 10. Considering that our sensor node will be supplied by a small battery of Y Volts with XmAh capacity, let us estimate the lifetime for a sensor node which is working on GPRS protocol. Considering that Lc is the average load current in Amperes, Bc the capacity of the battery in mAh, and the battery lifetime in Hours \(B_{l}\), then the battery life can be given by the battery capacity over the load current [40]  
\[B_{l}=\frac{B_{c}}{L_{c}} \tag{5}\]  
Basing on the Equation (5), \(L_{c}\) is the total current of the sensor node and is the summation of the current consumed by the sensor unit, microcontroller and radio unit. Then the Equation (5) can be written as:  
\[B_{l}=\frac{B_{c}}{C_{s}+C_{mcr}+C_{r}} \tag{6}\]  
where: \(C_{s}\) is the sensory unit current, \(C_{mcr}\) the microcontroller current, and \(C_{r}\) the radio unit current. In our experiment, we tried to record the current consumption of the radio unit and as we recorded for  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Location** & **Average RSSI [TA]** & **Average RSSI [dBm]** & **Average Current [A]** \\ \hline Location 1 & 19.2 & \(-75\) & 0.016378 \\ Location 2 & 15.2 & \(-83\) & 0.017014 \\ Location 3 & 30.8 & \(-53\) & 0.008890 \\ Location 4 & 24.3 & \(-65\) & 0.012646 \\ Location 5 & 19.7 & \(-73\) & 0.013699 \\ Location 6 & 25.48 & \(-63\) & 0.011800 \\ \24 h for each location, we are considering the average current for each location so, \(C_{r}\), the radio unit can be found using the following formula  
\[C_{r}=\frac{\sum_{n=0}^{k}i}{k} \tag{7}\]  
where: \(C_{r}\) is the average radio consumption current at a particular location, \(k\) number of measurement, and i the instantaneous current? During our experiment, as we recorded data for 24 h with a waiting time of 500 ms, we made 172,800 measurements for the whole day. Then referring to Equation (7), \(k\) = 172,800 and from Table 10, we have Pr form six different locations. Average currents in six different locations are summarized below:  
Current for Location 1 = 0.016378 A  
Current for Location 2 = 0.017014 A  
Current for Location 3 = 0.008890 A  
Current for Location 4 = 0.012646 A  
Current for Location 5 = 0.013699 A  
Current for Location 6 = 0.011800 A