esa-sceva/satcom-chunk-collection huggingface dataset:
# Autonomous Wireless Sensor Networks in
## Materials and Methods
### Sdss
This model consists of sigma fuzzy membership functions used to map the suitability corresponding to climatic thresholds limiting the ability of the vector to overwinter and reproduce during the active season. The climatic parameters (30-year averages for the reference period 1971-2000) that were selected as input to the model are: (i) Mean annual temperature; (ii) Total annual precipitation; (iii) Mean temperature for June, July, and August; (iv) Mean January temperature and (v) Precipitation frequency expressed as number of days within a year with rain >1 mm. Finally, we use NTL (night-time light) [41] remote sensed data as a proxy for human population density and outputs from a digital elevation model for estimating the vertical temperature gradient during the interpolation with the co-Kriging algorithm. The climate data were obtained from the WorldClim online database [37] with a resolution of 30 arc-seconds (\(\approx\)1 km). (b) _WSN as input to a dynamical Ae. albopictus population model_:  
For this paper, we looked at the output from a pilot test of the PentaSense system in a field site in Sint Truiden (Belgium) at the Pcfruit institute [42]. The nodes cover an area of approximately 1.4 ha. Temperature data has been collected from September 2018 and is still in place. The system consists of 4 PentaSense nodes using Lora and RF 866 MHz point to point communication and a smart Gateway with the RockBLock 9603 SBD Module and 3G communication Modem. The SDSS generates forcing time-series files based on input from the Wireless Sensor Network. The dynamic model consists of a nonlinear, coupled set of 10 prognostic ordinary differential equations focusing on the main phases in the life cycle of the pest [43]. The model is integrated with a daily time step using an implicit Adams numerical scheme and forced with temperature-dependent external parameters. Finally, we compare the WSN data that was used to force the mosquito population dynamics model with long term averages for the current climatology (reference period 1981-2010). For this, we used the ERA5 re-analysis data from ECMWF [44] which offers hourly time series of surface variables on a 30 km grid. The grid point nearest to the WSN was selected.
## 6 Prediction of Remaining Useful Life  
Damage diagnosis allows for the early detection of damage in structures and also indicates early deterioration in performance levels. The process of implementing a damage identification strategy in structures, which is termed as structural health monitoring (SHM), involves the observation of a structure in real-time using various sensors, the extraction of damage-sensitive features from measured data, and the performance of statistical analysis of these features to determine the current state of structure health [11]. Both damage diagnosis and SHM are required for identifying damage in structures, while the damage prognosis (DP) is an extension of damage diagnosis and SHM and is defined as \"the estimation of an engineered system's remaining useful life\" [196]. That is, based on the current state determined by damage diagnosis and SHM, DP is aimed to predict the structural future performance by estimating future loading environments and then predicting the remaining useful life through simulations and past experience.
## 1 Introduction
### Literature Review on the Inter-Satellite Routing Algorithm
Additionally, in recent years, some routing algorithms based on machine learning and deep reinforcement learning [23] have gradually gained attention in satellite network routing problems. Given that a mega constellation is highly complex and dynamic, artificial intelligence approaches may also present new opportunities for the design of routing algorithms for a mega constellation. Although the above studies have proposed many algorithms for the inter-satellite routing problem, most of them do not apply to the large-scale inter-satellite routing problem. Meanwhile, many studies have oversimplified the inter-satellite routing problem that cannot be used in practical applications, and these algorithms are still lacking in theoretical interpretation. Consequently, how to balance the complexity of the actual problem and the effectiveness of the routing algorithm is a very critical and urgent problem to be solved.
## 2 Materials and Methods
### Analysis
#### 2.2.2 Soil  
Before the experiment in February 2020, an initial composite soil sample of the area was taken. At the end of the experiments in May 2020 and in March 2021, composite soil samples were taken from each plot from 0-0.20 m of depth. Soil samples were air-dried, sieved (<2 mm) analysed for pH (H\({}_{2}\)O), electrical conductivity (EC), organic matter (OM), textural class and Olsen P. The pH was measured using a pH electrode (CRISON micropH 2002, Barcelona, Spain) by taking 10.0 g of dried and sieved soil and 25 mL of distilled water (1:2.5 soil:solution ratio) which were in contact for 1 h. The EC was measured with a conductivimeter at a soil:water suspension ratio of 1:2 after 60 min of shaking. According to the procedure described by [PERSON] and [PERSON], the organic matter was analysed using a potentiometric titration method (HI 901 C1, HANNA Instruments, Woonsocket, RI, USA), [30]. The soil texture was evaluated by the particle size analysis using the pipetting method [31]. Crop available potassium was quantified by [PERSON] et al. [17]. The Olsen P [16] was used to evaluate the available soil P. The orthophosphate P in the solution was determined by the molybdate blue method of [PERSON] (spectrophotometer Thermo Electron Corporation evolution 300 LC, Waltham, MA, USA), [19].  
The soil was sandy loam, and at the beginning of the experiment (Table 4), it was slightly acidic, high in OM, high in P and very high in K, with a high capacity of exchangeable cations, medium level of exchangeable Ca, low in Mg and very low in Na [18].  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Lettuce**} & \multicolumn{2}{c}{**Kale**} \\ \hline
**Treatment** & \multicolumn{2}{c}{**TKN**} & \multicolumn{2}{c}{**TKN**} \\ \hline
**Experiment** & \(\mathbf{N_{m}}\) & **NO** & \(\mathbf{N_{m}}\) & **NO** \\  & \(\mathbf{kg\ N\ ha^{-1}}\) & \(\mathbf{kg\ N\ ha^{-1}}\) & \(\mathbf{kg\ N\ ha^{-1}}\) & \(\mathbf{kg\ N\ ha^{-1}}\) \\ \hline Control & 0 & 0 & 0 & 0 \\ Ni85 & 85 & 0 & 85 & 0 \\ DG-N85 & 30 & 55 & 16 & 69 \\ DG-N170 & 59 & 111 & 32 & 138 \\ DG-N170+85 & 89 & 166 & 48 & 207 \\ DG-N170+170 & 119 & 221 & 65 & 275 \\ DG-N85+Ni60 & 90 & 55 & 76 & 69 \\ DG-N170+Ni60 & 119 & 111 & 92 & 138 \\ DG-N170+Ni25 & 84 & 111 & 57 & 138 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Amount of N applied to soil in the different treatments through mineral (\(\mathbf{N_{m}}\)) and organic (NO) forms.
# QoS-Aware Flexible Handover Management in Software-Defined Mobile Networks
## 3 QoS-Based Flexible Handover Scheme in SDN
### QoS-Based Flexible Handover Procedures in SDN
The following procedures for IP address configuration are the same as the Class 3 handover procedures (it can be observed that the multicasting of the DL flow of the MN to candidate ASs can be set at the CS when the controller updates the flow entry at the CS in order to reduce the DL handover delay. Using the multicasting in the proactive procedure depends on the network operator's policy because there is a tradeoff between resource efficiency and fast DL handover).
# Is Satellite Ahead of Terrestrial in Deploying NOMA for Massive Machine-Type Communications?
## 4 Developed E-SSA-Based NOMA System Elements
### Fixed Terminals  
The F-SIM protocol has been integrated into the innovative all-in-one terminal concept named \"SmartLNB\". In collaboration with Eutelsat, two manufacturers (Egated and Ayecka) have conceived and produced terminals that are now available as commercial products (see Figure 7), with a lower price with respect to competitive two-way satellite terminals. The user terminal key technical characteristics are as follows:  
* Integrated modem and low-noise block (LNB)/block upconverter (BUC) into an integrated outdoor unit (ODU) design (15 \(\times\) 11 \(\times\) 3 cm, 1.3 kg);
* Coaxial or Ethernet connection from ODU to indoor unit (IDU) with data and power supply sharing the same cable;
* Ku-band linear polarization;
* Maximum transmit output power: 27 dBm, resulting in 36 dBW effective isotropic radiated power (EIRP) with a typical 70 cm dish;
* Support of all DVB-S2X MODCODs;
* Modem based on the ST Cardiff3 chipset;
* Power consumption: 0.5 W (standby), 7 W (receive only), 16 W (receive and transmit);
* Based on Linux operating system, and supporting TCP/IP, VLAN, VRF, IPSEC and DHCP.  
Easy installation and commissioning using a smartphone app (available for iOS and Android).  
The Egated Gen3 terminal has also been integrated into a KNS A6-MK2 maritime antenna (see Figure 8), with good results in terms of quality of service. This opens the way to new IoT-type of services on small boats, with a terminal cost much lower than typical two-way terminals.  
Figure 7: SmartLNB Gen3 terminals from Egated (**a**) and Ayecka (**b**, mounted on a dish).
# The Study on Internal Flow Characteristics of Disc Filter under Different Working Condition
## 2 Materials and Methods
### Experimental Design  
The platform used for the freshwater filtering test was shown on Figure 1. The test involved three types of DFs and three kinds of flows. Every filter element was equally divided into three parts by disc number, the upper part was UP, the middle part was MID, and the lower part was DOW. Each part was wrapped with a plastic film, which was assumed to be a complete blockage, as shown on Figure 1. The abbreviated number was KD1 UP, KD1 MID, KD1 DOW, KD2 UP, KD2 MID, KD2 DOW, KD3 UP, KD3 MID, KD3 DOW, and CK. CK is a freshwater filtering test without a filter bed, and KD1, KD2, and KD3 is the head loss of different DFs not wrapped with plastic film. The flow was measured using a electromagnetic flowmeter (China, DN100, 4 V, 0150 \(\pm\) 0.1 m\({}^{3}\)/s), and the pressure was measured by a pressure gauge (EIECALL, 0-6 \(\pm\) 0.1 KPa). Head loss was evaluated by the pressure difference before and after DF, and it was analyzed at different flows (5, 10, 20, 25, 30, and 35 m\({}^{3}\)/h) of freshwater. \(T_{KD_{1q}}\) indicates the proportion of head loss between DFs wrapped in plastic film (UP, MID and DOW) and DFs not wrapped in a plastic film. The calculation is shown in Equation (1).  
\[T_{KD_{iq}}=\frac{\left(KD_{iq}-KD_{i}\right)}{KD_{i}} \tag{1}\]  
\(q\) was UP, MID, and DOW; \(KD_{i}\) indicates the head loss of the whole filter, and \(i\) was 1, 2, and 3;  
The flowmeter and pressure gauge were set and calibrated by the manufacturer before the formal experiment. From Figure 1, the globe valve was used to adjust the flow to achieve the experimental flow. The pressure was recorded after the flowmeter was steady. The difference between front and rear pressure is the DF head loss.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Nov.** & **Test Items** & **Value** \\ \hline
1 & pH & 7.8 \\
2 & Total suspended solid (TSS) mg/L & 7.7 \\
3 & Total hardness mg/L & 983 \\
4 & Chemical Oxygen Demand (COD) mg/L & 0.74 \\
5 & Chloride mg/L & 167 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The quality of the water in the clean water test.  
Figure 1: (**a**) Testing disc filter and (**b**) freshwater testing platform. a sedimentation tank, b pumping tank, c submersible pump, d globe valve, e disc filter, f pressure gauge, and g flowmeter.
# Adaptive PI + VPI Harmonic Current Compensation Strategy under Weak Grid Conditions
## Influence of Weak Grid Conditions on the APF System  
### Three-Phase SRF-PLL and APF Control System  
Figure 1 presents the control structure of a typical APF. The main circuit of the APF is composed of a voltage source three-phase conversion circuit. The AC side is connected to the common connection point by the output filter inductance, and the DC side is a capacitor, which can maintain the voltage of the DC side by storing the electric field energy. The control loop mainly includes a synchronous reference coordinate phase-locked loop module, DC side voltage regulation module, reference current generation module, current tracking control module and SVPWM module.  
The three-phase SRF-PLL is responsible for locking the phase and frequency of the three-phase grid voltage at the point of common coupling. The above information is provided to the harmonic reference current generation (RCG) module and the current tracking control module. The DC-side voltage regulation module is responsible for generating the active current reference value that maintains the DC-side voltage. The reference value plus the harmonic reference current detected by the instantaneous power theory is used to obtain the reference current to be compensated. The accuracy of the above reference current depends on the accuracy of the phase angle estimation provided by the three-phase SRF-PLL.  
According to the difference between the actual output current and the reference current of the APF, the current tracking control calculates the reference voltage of the AC side of the converter and further realizes the drive of inverter switching devices through the SVPWM module, controls the harmonic and reactive power compensation current required for the APF output and nonlinear load operation and realizes the compensation of harmonic and reactive power. It is worth noting that the phase angle information accuracy of the phase-locked loop module output will directly affect the current tracking control algorithm in the rotating coordinate system, and the frequency information accuracy of the phase-locked loop module output will affect the resonant current tracking control algorithm, such as PR control and VPI control.  
The accuracy of the phase angle and frequency of the grid-side voltage of the three-phase SRF-PLL can directly affect the performance of the harmonic current detection and current tracking control algorithm in the rotating coordinate system, especially the performance of the resonant current control, thus affecting the harmonic compensation performance of the APF system.
# A Topology Based Automatic Registration Method for Infrared and Polarized Coupled Imaging
## 3 Results and Discussion
### Objective Evaluation  
All the following registration results for comparisons are given by the intensity-based registration algorithms. The running time and Intersection of Union (IoU) are used as indicators for comparison. Intensity-based algorithms could not register the original image directly. The time indicators are calculated from the running duration experiment of the segmented image. The average time for image segmentation was 10.73 s on our device, which was excluded from the comparison.  
The accuracy rate of registration was calculated by the IoU formula as follows:  
\[\text{IoU}=\text{Area of Intersection}/\text{Area of Union }=(A_{fixed}\cap A_{ moved})/(A_{fixed}\cup A_{moved}) \tag{9}\]  
ranging from 0 to 1. \(A_{fixed}\) and \(A_{moved}\) indicates the region of targets in the polarized image and infrared image. When the targets in the two images are completely overlapped, this means the two images are fully registered, the IoU is 1.  
Since the multi-modal operator MIND registration result cannot be used for IoU calculation, it is omitted here. Additionally, it takes 154.41 s to register the synthetic image and 16.17 s to register the real-world image. It can be seen from Table 3 that the performance of the SSR algorithm is the best in the infrared and polarization parameter registration experiments, except for manual registration in the real-world experiment, which took more than 15 min to process.  
Figure 8: (a) SSR, (b) Manual, (c) initial pair, (d) BSO, (e) GA, (f) Powell, (g) PSO.  
For binary images with obvious consistent gray areas, registration methods based on intensity can be tested, such as BSO and GA. Those methods are mostly time-consuming, and lack robustness for distortion and field curvature in images taken by different cameras. In contrast, our SSR can be endowed with high operation speed and robustness to optical distortion by integrating skeleton endpoints as the control points for image transformation.  
A total of 30 pairs of real-world images were registered by SSR. The IoU results were shown in Figure 9. It can be seen that SSR performed good registration in all the pictures. Since the IoU is affected by the segmentation results, the larger deviation in one of the scenes is attributed to the impure segmentation. In our practical applications, SSR performed well enough to meet the registration needs of polarization parameters and infrared images.
## 5 Conclusions  
Single-hop and multi-hop FSO configurations were investigated with respect to PSK signals employing subcarrier intensity modulation. Atmospheric turbulence described by the generic \(M\)(alaga) model, along with non-zero boresight misalignment and phase noise impairments, were taken into account. Considering all of these effects, mathematical expressions for the average symbol error probability and the mean outage duration were derived. The obtained results demonstrate significant performance losses as atmospheric turbulence, non-zero boresight pointing errors, and phase noise effects are getting stronger, as more complex PSK formats are used, and as link length and the number of DF relays increase for the same total link distance. The validity of the derived mathematical expressions was properly verified through simulation results.  
The main idea, the theoretical part of the manuscript and the conclusions have been studied by [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] while the numerical results section has been investigated by [PERSON].  
The authors declare no conflict of interest.  
Figure 5: Mean outage duration (MOD) per hour versus normalized average electrical SNR of single-hop or dual-hop FSO configurations for weak turbulence strengths, as well as weak and moderate amounts of non-zero boresight pointing errors.
## 2 Materials and Methods
### Methods
#### 2.3.2 Standardized Precipitation Evapotranspiration Index (SPEI)
After calculating the SPEI at different timescales (1, 3, 6, and 12 months), we conducted statistical comparison using the observed precipitation data, the CHIRPS precipitation estimates, and the temperature data from Era5-Land reanalysis. This evaluation covered 18 meteorological stations and employed statistical metrics such as BIAS, \(r\), and RMSE in pixel-to-point comparison. We also performed significance tests at the 5% level (\(p\)-value \(<\) 0.05) to assess the statistical reliability of the results. Furthermore, we generated SPEI maps from the CHIRPS satellite data and compared them to observed SPEI maps using the Inverse Distance Weighting (IDW) interpolation method, which has been widely validated in previous studies [7, 49, 50, 61, 60]. This analysis provides a detailed overview of the drought patterns and confirms the reliability of the satellite data.
# Numerical Analysis of Friction Reduction of Grouting in Long-Distance Pipe Jacking
## 2 Theoretical Calculation Methods for the Jacking Force
### The Friction Resistance  
When the circumferential friction of the pipe is uniformly distributed, it can be calculated using Equation (3) according to the regulation of China [13]:  
\[P_{f}=\pi D_{1}Lf_{k} \tag{3}\]  
In the formula,  
\(D_{1}\) is the outer diameter of the pipe (m);  
\(L\) is the jacking length of the pipe (m);  
\(f_{k}\) is the average unit frictional resistance between the pipe's outer surface and the soil (kN/m\({}^{2}\)). The specific values are shown in Table 1[13].
# Rotor Location During Atrial Fibrillation: A Framework Based on Data Fusion and Information Quality
###### Abstract  
Persistent atrial fibrillation (AF), a prevalent cardiac arrhythmia, is primarily sustained by rotor-type reentries, with their localization crucial for successful ablation treatment. Fractionated atrial electrogram (EGM) signals have been associated with the tips of the rotors and are thus considered as ablation targets. However, the typical noise problems of physiological signals affect the results of EGM processing tools, and consequently the ablation outcome. This study proposes a data fusion framework based on the Joint Directors of Laboratories model with six levels and information quality (IQ) assessment for locating rotor tips from EGMs simulated in a two-dimensional model of human atrial tissue under AF conditions. Validation tests were conducted using a set of 13 IQ criteria and their corresponding metrics. First, EGMs were contaminated with different types of noise and artifacts (power-line interference, spikes, loss of samples, and loss of resolution) to assess tolerance. The signals were then preprocessed, and five statistical features (sample entropy, approximate entropy, Shannon entropy, mean amplitude, and standard deviation) were extracted to generate rotor location maps using a wavelet fusion technique. Fuzzy inference was applied for situation and risk assessment, followed by IQ mapping using a support vector machine by level. Finally, the IQ criteria were optimized through a particle swarm optimization algorithm. The proposed framework outperformed existing EGM-based rotor detection methods, demonstrating superior functionality and performance compared to existing EGM-based rotor detection methods. It achieved an accuracy of approximately 90%, with improvements of up to 10% through tuning and adjustments based on IQ variables, aligned with higher-level system requirements. The novelty of this approach lies in evaluating the IQ across signal-processing stages and optimizing it through data fusion to enhance rotor tip position estimation. This advancement could help specialists make more informed decisions in EGM acquisition and treatment application.  
Keywords:data fusion; electrogram; information quality; JDL model +
Footnote †: journal: Applied sciences
# Inverse Solution Error Analysis and Correction of Beam Steering System Based on Risley Prisms
## 2 Error Model of Risley Prisms and Forward Solution Error Correction Method
### Error Parameter Identification  
On the basis of the error model, the exact or equivalent values of errors can be obtained by parameter identification [17]. As shown in Equation (8), the error model describes the relationship between prism rotation angles \(\theta_{1},\theta_{2}\) and errors and the deflection angle and azimuth of the outgoing beam \(\Phi,\Theta\).  
\[f(\theta_{1},\theta_{2},\delta\theta_{1},\delta\theta_{2},H_{I},V_{I},H_{P},V_ {P},H_{R},V_{R})=(\Phi,\Theta) \tag{8}\]  
The essence of the parameter identification method is to fit the exact or equivalent value of the error through the experimental data.  
Among the several systemic errors in the error model, the incident light tilts \(H_{I}\), \(V_{I}\) and prism rotation angle errors \(\delta\theta_{1}\), \(\delta\theta_{2}\) are constants, while the bearing rotation axis wobble is a systematic error that changes periodically with the rotation of the bearing. Therefore, we set the prism rotation axis tilt \(H_{R}\), \(V_{R}\) as a function of the prism rotation angles \(\theta_{1}\) and \(\theta_{2}\). After the prism is mounted on the bearing, the angle between the prism and its rotation axis is a fixed value, so we use the rotation axis tilt to represent the prism tilt \(H_{P}\), \(V_{P}\). With sufficient data and a suitable optimization algorithm, the exact or equivalent value of the errors shown in Equation (8) can be fitted by the least-squares method.