esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Feedback and Inoculum Characteristics
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Parameters (Units)** & **SBB** & **CM** & **PM** & **Inoc1** & **Inoc2** \\ \hline pH & \(5.7\pm 0.7\) & \(6.2\pm 1.3\) & \(6.5\pm 1.7\) & \(7.5\pm 0.7\) & \(7.6\pm 0.8\) \\ TS (g/kg) & \(880.3\pm 12.4\) & \(22.7\pm 11.5\) & \(225.3\pm 7.4\) & \(41.2\pm 1.3\) & \(36.1\pm 10.6\) \\ VS (\%TS) & \(90.6\pm 1.8\) & \(77.7\pm 6.7\) & \(75.1\pm 5.2\) & \(16.3\pm 1.6\) & \(21.8\pm 3.3\) \\ CODS (gO\({}_{2}\)/kg TS) & \(64.7\pm 2.5\) & \(77.7\pm 13.9\) & \(83.0\pm 7.9\) & & \(246.5\pm 6.1\) \\ CODT (gO\({}_{2}\)/kg TS) & \(165.8\pm 5.9\) & \(308.5\pm 6.3\) & \(220.6\pm 11.1\) & & \(321.3\pm 6.9\) \\ DOC (gC/kg TS) & \(49.7\pm 5.1\) & \(33.7\pm 9.8\) & \(39.1\pm 6.6\) & \(150\pm 5.0\) & \(127.4\pm 19.4\) \\ TVFA (gLx/kg TS) & \(2.5\pm 0.9\) & \(24.3\pm 4.9\) & \(20.4\pm 2.2\) & \(7.2\pm 2.4\) & \(47.1\pm 4.3\) \\ Alkalinity (gCaCO/kg TS) & \(3.3\pm 1.4\) & \(170.2\pm 0.9\) & \(169.9\pm 49.3\) & & \(116.4\pm 69.3\) \\ N-NH4+ (gN/kg TS) & \(0.3\pm 0.1\) & \(15.7\pm 3.2\) & \(10.6\pm 2.6\) & & \(30.5\pm 5.5\) \\ TN (gTN/kg TS) & \(14.5\pm 1.5\) & \(157.6\pm 6.7\) & \(145.2\pm 12.4\) & & \(174.5\pm 3.6\) \\ Ratio C/N & \(38.9\pm 2.7\) & \(13.2\pm 1.0\) & \(13.5\pm 0.3\) & - & \(19.4\pm 2.4\) \\ Hemicululose (\%) & \(15.4\pm 0.3\) & \(13.9\pm 0.8\) & \(14.4\pm 0.1\) & - & - \\ Cellulose (\%) & \(25.9\pm 0.3\) & \(23.2\pm 1.2\) & \(16.3\pm 1.2\) & - & - \\ Lignin (\%) & \(1.6\pm 0.1\) & \(19.8\pm 1.1\) & \(16.8\pm 1.9\) & - & - \\ Mineral salts (\%) & \(6.7\pm 0.2\) & \(26.2\pm 1.0\) & \(27.2\pm 3.1\) & - & - \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Physicochemical properties of sugar beet by-products (Sugar beet by-products, cow manure, pig manure), and inocula.
# Probing Dust and Water in Martian Atmosphere with Far-Infrared Frequency Spacecraft Occultation
## 2 Materials and Methods
### Atmospheric Losses
#### 2.2.2 Dust and Water Ice clouds  
Dust particles and water ice aerosols cause absorption and scattering of electromagnetic radiation leading to loss of signal. Expected losses can be estimated from Rayleigh scattering and absorption efficiencies for atmospheric aerosols.  
\[Q_{ext} = Q_{abs}+Q_{sca} \tag{7}\] \[\alpha = 4.343Q_{ext}N\pi r_{eff}^{2} \tag{8}\]  
Figure 1: Illustration of spacecraft occultation geometry. The ray path from transmitter to receiver undergoes refraction in Martian atmosphere at a bending angle \(\delta\) at a given level in atmosphere with a distance from the center of planet \(r\) and ray impact parameter \(a\).  
where \(Q_{ext}\) is the extinction efficiency, which can be expressed as the sum of absorption \(Q_{abs}\) and scattering efficiencies \(Q_{scai}\); \(\alpha\) is attenuation coefficient; N is the number density of dust particles; and \(r_{eff}\) is the effective dust radius. A projection of expected signal loss during transmission of rays in occultation geometry is given by the following expression [39]:  
\[\tau(r_{j})=2\int_{r_{j}}^{\infty}\frac{\alpha(r)}{\sqrt{1-(a(r_{j})/r\mu(r))^ {2}}}\,dr \tag{9}\]  
where \(\tau\) is the expected loss, \(\alpha\) is the attenuation coefficient, \(a\) is the impact parameter of the ray in the geometry of occultation [39; 40], and \(r\) is the radial distance from the center of the planet to the point of observation.
## 4 Health Prognostics
### Rul  
RUL estimates the remaining service life of a component or system that can operate according to its expected function before maintenance or replacement [212; 213], which is \(RUL_{k}=t_{\mathit{eof}}-t_{k}\), where \(t_{k}\) is the current time, \(t_{\mathit{eof}}\) is the end of life of the equipment, and \(RUL_{k}\) is the RUL at \(t_{k}\). An important feature of ship intelligence is self-awareness and self-prediction ability, in which RUL prediction plays an important role. RUL prediction is currently the core part of system PHM and CBM [214]. The major task of RUL prediction is to forecast the time left before the machinery losses its operation ability based on the condition monitoring information. It is the last technical process as well as the ultimate goal of machinery HP [154]. MSAE is a kind of system with low frequency and high consequences. Accurate RUL prediction of MSAE can predict the safe operation of the ship and the time when the system equipment performs its expected functions, and then guide it to achieve predictive maintenance to reduce expensive unplanned maintenance [215; 216; 217]. The main difference between its RUL prediction and other engineering systems lies in its complex working environment and diverse operation modes [218]. Commercial shipping currently relies more on inefficient, traditional time-based maintenance procedures, resulting in poor economical and labor-intensive [212]. Ship failure and shutdown caused by MSAE failure will bring potential safety hazards and cause substantial economic losses. Appropriate maintenance actions can be arranged in advance through a practical PHM framework, timely detection of failures, and effectiveness to avoid catastrophic failures and reduce maintenance costs [219].  
RUL prediction process could be realized through direct and indirect methods [175; 220; 221; 222]. The direct calculation process takes the machine history and current information as input to establish the relationship model between information and RUL. This method does not need to determine the fault threshold in advance and can directly estimate RUL. The indirect calculation process first needs to determine the failure threshold, establish the HI and health condition model, and finally calculate the time when HI reaches the threshold.  
Based on different technologies and methodologies, we subdivide the methods of RUL prediction into three different categories, that is, degradation model methods, machine learning model methods, and hybrid model methods [223; 224; 225; 226], as depicted in Figure 11.
## 3 Results and Discussion
### Discussion  
The main features to highlight in the analyses carried out are:  
* the type of corrosion defects observed, which are mainly pit-type defects,
* the presence of gas pockets detected in some of the pits,
* the development of the process in a short time, before the piping system was put into service, and about five months after its construction,
* the lack of water circulation in this period, as well as the evidence of the existence of stagnant water,
* the presence of sulfur in the corrosion deposits, as revealed by the SEM semi-quantitative analysis,
* the presence of sulfate-reducing bacteria (SRB), as revealed by the bacterial culture, and,  
Figure 12: Organisms (probably SRB) observed in the culture.  
the absence of other obvious signs of aggressiveness of the environment or defects in the material.  
Thus, based on both the morphology of the corrosion damage observed and the evidence of the presence of sulfate reducing bacteria (SRB), microbiologically induced corrosion (MIC) by SRB seems to be the fundamental cause of the process suffered by the pipes being analyzed.  
Sulfate-reducing bacteria (SRB) comprise several groups of bacteria that use sulfate (which in this case must be present in the water) as an oxidizing agent, reducing it to hydrogen sulfide H\({}_{2}\)S, which is a gas with a high corrosive capacity. The process generally takes place under anaerobic conditions, favored by the formation of a sheet of biological material itself (biofilm) or other elements of the system (sludge, coatings, oils). The accumulation of H\({}_{2}\)S gives rise to the formation of pockets [23], which justify the existence of bubbles and also tubers as the ones observed in Figure 6.  
There are many references in the literature reporting failures caused by MIC corrosion in different types of stainless steels [24; 25; 26; 27; 28; 29] and, in particular, in AISI 316L grade [30; 31; 32; 33; 34]. These references share typology, characteristics, and a failure time range with the deterioration process analyzed here.  
The analysis performed here also allows different recommendations to be provided in order to avoid similar problems. The main one consists of avoiding the presence of stagnant water in the system, given that this is the final cause of the MIC corrosion process. Additionally, other alternatives, such as the use of inhibitors, could be considered, as it has been successfully applied in other corrosive contexts (e.g., [35; 36]).  
Finally, when performing this kind of failure analysis where corrosion processes are clearly involved, the use of electrochemical analyses could be considered to clarify the root cause of the failure and the material tendency to develop this kind of damage (e.g., [37; 38]).
## 3 Results and Discussion
Generally, electrical conductivity is sensitive to multiscale defect scattering carriers, including dislocations, pores, grain boundaries, and point defects in thin films [22; 54]. Point defects, which can be formed by growth conditions, directly affect the electrical conductivity of the thin films. Figure 6a shows that the carrier concentrations were on the order of \(10^{20}-10^{21}\) cm\({}^{-3}\) under all conditions. Among the CIGS:Te thin films co-sputtered with Te to the CIGS thin film, the two conditions of (2) and (6) with relatively high carrier concentrations were consistent with the conditions with poor surface properties in Figure 1. A higher carrier concentration was inferred to be caused by higher potential barriers that originated from small crystallites with larger imperfections [51; 55]. This is because under condition (6), the CIGS:Te thin films have the highest In + Ga surplus and the largest Se + Te vacancies, resulting in the largest number of defects and, as a result, the highest carrier concentration, whereas under condition (3), with the closest stoichiometry, the CIGS:Te thin films show the smallest number of defects and, as a result, the smallest carrier concentration. This is also consistent with the conditions under which the microstrain and dislocation density are high, as shown in Figure 4c. The carrier mobility is on the order of \(10^{-1}\) cm\({}^{2}\)/V\(\cdot\)s, as shown in Figure 6a, which is similar to or slightly higher than previously reported values for CIGS thin films owing to the replacement of Se vacancies by Te, which do not act as point defects or impurities. The carrier mobility under conditions (2) and (6) decreases suddenly because the large gaps interfere with the carrier's movement from a grain to its neighboring grain. After Te co-sputtering, the resistivity generally decreases to the range of \(10^{-2}\)-\(10^{-1}\)\(\Omega\cdot\)cm. Resistivity in the thin films depends mainly on the carrier concentration and mobility and obeys the relationship \(\rho=1/nq\mu\). Under condition (4), a slight increase in resistivity was observed, which is correlated to lower carrier mobility with a consistent carrier concentration, that is, the number of imperfections. Figure 6: (a) Carrier concentration (\(n\)), carrier mobility (\(\mu\)), and (b) resistivity (\(\rho\)) in the CIGS:Te thin films with different Te co-sputtering times of (1) 0, (2) 270, (3) 510, (4) 750, (5) 945, and (6) 1140 s.
## 3 System Concept
### RF Concept and Design
#### 3.1.4 Analog Baseband  
The voltages obtained by the detector, also called baseband voltages \(B_{3,\ldots,6}\), are then further conditioned in the analog domain. Because the power detectors provide a large bandwidth, analog filters are required to reduce the noise, which is outside the frequency band of interest. Therefore, two first-order lowpass filters with a cutoff frequency of \(120\,\mathrm{H}\mathrm{z}\) are added before and after an amplifier, respectively. The amplifier has a digitally adjustable gain and controls the different baseband voltages such that they will make full use of the following analog-to-digital converter's (ADC) dynamic range of 0-4 V.
# Adaptive Handover Decision Using Fuzzy Logic for 5G Ultra-Dense Networks
## 3 Proposed FLDHDT
### Adaptive Handover Decision Using Fuzzy Logic
#### 3.2.2 Inference Using Fuzzy Logic  
The general architecture of an FL controller comprises three components, namely a fuzzifier, rule base and inference engine, and defuzzifier [26]. As depicted in Figure 5, the inputs to the fuzzifier used in FLDHDT are the SINR and horizontal speed of the UE, and the outputs of the defuzzifier are the HOM and TTT values for the UE.
# Development of a Virtual Reality-Based System for Simulating Welding Processes
## 2 Materials and Methods
### Development of the Virtual Reality Module (Module #3)  
To build the virtual reality environment for describing the SMAW, MIG, and TIG welding processes, it was important to analyze the mechanisms of these welding processes to obtain the main parts of each welding process (Figure 11). Based on analysis of the SMAW, MIG, and TIG welding processes, the 3D CAD models of these welding processes were designed using CatiaTM tool. These models were converted to the STEP or IGES format. Then, using the Unity3D tool, we were able to obtain these models in the virtual environment.  
Figure 11: From 3D CAD models to virtual reality environment.  
Figure 10: Parameters for simulating the welding process.  
Unity3D was used as a virtual reality development platform supporting three scripting languages, including JavaScript, C#, and Python, as well as supporting the Windows platform, VB.net, VB6, Delphi, and other programming languages [21; 38]. In this work, the programming language C# was used for programing the virtual environment. To achieve the models and scenes in the virtual environment, the 3D models were imported into the Unity3D. Then, a program was generated to achieve the objects and scenes [21]. Modeling plays an important role in the VR system. To generate 3D models and scenes for describing the SMAW, MIG, and TIG welding processes, parts of each welding component were scanned using a 3D scanner machine. From the scanned data, which were in point cloud format, the 3D models were generated using \(\text{Catia}^{\text{TM}}\) software.  
For generating the virtual reality environment, an algorithm was proposed as shown in Figure 12 with two stages: (1) modeling the 3D models, which show components of the SMAW, MIG, and TIG welding processes and (2) converting these models to virtual reality modeling language (VRML) models [28; 39]. To implement the VR module, this research used Unity3D platform with C# language for programming to draw the weld joint as well as describe the welding process as detailed in Appendices K and L, respectively. Some scenes in Unity3D were employed by adding UnityEngine.dll as presented in Appendix K.  
For connecting module #3 to other modules, the function for transferring the data from modules #1 and #2 to module #3 via the RS485 port is described detail in Appendix I. The function for module #3 obtaining the data from other modules is shown in Appendix J.
## 4 WEAMR Overview
### Internode Communications  
We have divided the network into different clusters and each cluster is associated with a default gateway, then routing of packets and route discovery is done within the cluster using RREQs. Our approach of intra-cluster routing is closer to the approach suggested in [13] (we recommend OSPF for gateway-to-gateway routing, as will be explained in Section 3.4). All the traffic (inter/intra-cluster and to/from the Internet) is routed through the gateway. The only exception here is the border nodes which are discussed later. Instead of an end-to-end wireless path, a packet is now routed through wireless-wired-wireless path, where the wired path is the communication between gateways via hotline and the wireless paths are used for intra-cluster communication. This approach clearly enhances the reliability of an end-to-end transmission. Under traditional routing, the path discovery process will discover an end-to-end wireless path. This path is less reliable, as shown in Figure 3, because the packet will traverse many wireless hops and with every hop there is an inherent threat of packet loss due to a variety of reasons like channel errors, collisions, and dead or sleeping nodes [4]. Moreover, under this approach, a number of sensor nodes are acting as relaying nodes. Consequently, more energy is consumed on sensors which results in a shortened network lifetime.
## 4 Results and Discussion  
The bearing force distribution, needle tooth force, and loaded transmission error of the cycloidal gear pair were generated by the aforementioned computation model. Table 1 shows the fundamental design parameters assuming that the gear pair's material characteristics are the same, Poisson's ratio is 0.3, and Young's modulus is 206 GPa.  
From the results of the force analysis, it was found that the angular displacement and reduction ratio after flank profile modification are in error from the theoretical values when it is in operation. In this work, the design is optimized for the maximum reduction ratio error of \(\bigtriangleup\kappa\) in the motion state, and the design variables are the toothing trimming parameters \(\bigtriangleup R_{p}\) and \(\bigtriangleup R_{rp}\). The combinations of the tooth profile correction parameters are used for the two combinations previously mentioned: (+ \(\bigtriangleup R_{p}\), + \(\bigtriangleup R_{rp}\)) and (- \(\bigtriangleup R_{p}\), - \(\bigtriangleup R_{rp}\)). The optimization tool used is the Matlab built-in optimization function fmincon.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Description** & **Value** \\ \hline \(n_{c}\) & Cycloidal tooth number & 9 \\ \(n_{p}\) & Roller number & 10 \\ \(M\) & Output pin number & 6 \\ \(r_{w}\) & Output pin position radius & 18 mm \\ \(e\) & Eccentricity & 2 mm \\ \(B\) & Gear width & 8 mm \\ \(R_{p}\) & Roller position radius & 30 mm \\ \(R_{rp}\) & Roller radius & 3 mm \\ \hline \hline \end{tabular}
\end{table}
Table 1: The fundamental design parameters of the cycloidal gear pair.  
Figure 4: The schematic diagram of the relationship between indentation depth and cycloidal gear rotation angle.
# Kernel-FastICA-Based Nonlinear Blind Source Separation for Anti-Jamming Satellite Communications
###### Abstract  
Satellite communication systems, as a core component of global information infrastructure, have undergone unprecedented development. However, the open nature of satellite channels renders them vulnerable to electromagnetic interference, making anti-jamming techniques a persistent research focus in this domain. Satellite transponders contain various power-sensitive components that exhibit nonlinear characteristics under interference conditions, yet conventional anti-jamming approaches typically neglect the nonlinear distortion in transponders when suppressing interference. To address this challenge, this paper proposes a kernel-method-optimized FastICA algorithm (Kernel-FastICA) that establishes a post-nonlinear mixing model to precisely characterize signal transmission and reception processes. The algorithm transforms nonlinear separation tasks into high-dimensional, linear independent-component-analysis problems through kernel learning methodology. Furthermore, we introduce a regularized pre-whitening strategy to mitigate potential ill-conditioned issues arising from dimensional expansion, thereby enhancing numerical stability and separation performance. The simulation results demonstrate that the proposed algorithm exhibits superior robustness against interference and enhanced generalization capabilities in nonlinear jamming environments compared with existing solutions.  
Kernel-FastICA; nonlinear blind source separation; satellite communication anti-jamming; interference suppression Article
## 2 Underwater Wireless Sensor Network
### Underwater Environment  
Firstly, most of the applications utilize three-dimensional network topology due to the three-dimensional nature of seas' and oceans' environment. More precisely, employing three-dimensional network topology in UWSNs requires a large number of sensor nodes. Therefore, underwater networks employ sparse network topology due to expensive underwater equipment, and the use of a vast number of nodes causing high network costs [20; 22; 28]. Secondly, the random movement in different depths and the free movement of sensor nodes by water currents in UWSNs causes dynamic network topology [29; 30]. Moreover, the vertical movement has been reported to be negligible, while it is indicated that the horizontal movement is about 1-3 m/s [2; 31]; Thirdly, it is difficult to replace and charge the battery of sensor nodes given the harshness of underwater environments. This issue has a direct effect on energy efficiency in underwater environments [2; 29; 31]. Lastly, the use of a global positioning system (GPS) is not possible in underwater environments due to the 1.5 GHz radio frequency used in GPS, which can be rapidly absorbed in water [2; 17; 29].
# Comparison of Oil Spill Classifications Using Fully and Compact Polarimetric SAR Images
## 2 Methods
### Supervised Classifications  
Supervised classifications can take advantage of training data samples to set up the decision rule for classification, which has the best capability of fitting training datasets, as well as predicting the class of testing data samples. In this paper, three largely used supervised classifiers are considered.
# Machine-Learning-Based Rollover Risk Prediction for Autonomous Trucks: A Dynamic Stability Analysis
### Novelty of This Study  
This study uniquely combined large-scale dynamic stability simulations (based on international test protocols) with machine-learning-based rollover risk prediction. Unlike previous research, it focuses on adjusting regulatory safety margins post-ESC installation, providing a framework for quantitative evaluation of maximum safe slope relaxation. The study also investigates input variable transformation (curvature and lateral acceleration) for improving machine learning model performance.  
To address these objectives, this study conducted dynamic simulations across standardized test scenarios (circle, double lane change, slowly increasing steer, J-turn, sine with dwell, and fishhook maneuvers) for five different truck configurations. Furthermore, machine learning models--including multiple linear regression (MLR), random forest, and recurrent neural networks (RNN)--were developed to predict the LTR index based on driving parameters. The comparative performance analysis highlights the effectiveness of RNN models for rollover prediction tasks.