esa-sceva/satcom-chunk-collection huggingface dataset:
# Microbial Biopolymers: From Production to Environmental Applications--A Review
## 2 Microbial Polymers
### Polyhydroxyalkanonates (PHA) and Polyhydroxybutyrate (PHB)  
PHA and PHB come under the type of polyesters that are intracellularly extracted by both bacterial and fungal strains. PHB is one of the forms within PHA. Among both, PHA attracts more interest from both industrialists and academia because of its wider range. PHB has limited flexibility, poor thermal stability, and a slow degradation rate as compared to PHA. Worldwide, many investigators have isolated PHA and PHB with the aid of diverse strains (Tables 1 and 3). PHA is usually comprised of linear-chained repeating (R)-3-hydroxy fatty acid units adjoined together by ester bonds. PHA is categorized into short chains (C3-C5) and medium chains (C6-C14) as per their carbon chain length. [PERSON] et al. [212] and [PERSON] et al. [21] extracted PHA by both submerged and solid-state fermentation. The higher yield was conveyed by solid-state fermentation. Moreover, a significant difference was noted in its thermal, structural, and morphological properties. Also, the selection of a raw material as a carbon source directly influences the quality of the microbial biopolymer. PHA functions as a storage unit and serves as a terminal electron receptor for bacteria under stress conditions [213; 214]. PHA is effectively being employed for the formation of bioplastic due to its biodegradability, thermoplasticity, and bio-tolerance attributes [215; 216]. [PERSON] et al. [217] developed PHB nanoparticle-based scaffolds and concluded its benefits for tissue engineering. Meanwhile, PHA could be regarded as an alternative to conventional plastics, which supports diminishing the use of synthetic polymers. [PERSON] et al. [218] explored the potential of PHA-based degradable much film in rice-seed germination. [PERSON] et al. [219] designed a protease biosensor based on AL-PHA beads for the detection of proteolytic activity. [PERSON] et al. [220] analyzed the long-term potential of diverse types of PHA-based material for bioremediation. Apart from this, PHA can also be used for purification processes [221, 222]. [PERSON] et al. [53] observed the biodegradability of both PHA and PHB in seawater by calculating the amount of carbon dioxide collected from decomposition operations. The main drawbacks of producing this microbial biopolymer are the expensive feed and inadequate thermal and mechanical properties, mainly of PHB. Improvements to the substrate types, supplying methods, growth conditions, and/or genetic modifications can lead to much improved traits [223].
# Deep Learning-Based Optimization for Maritime Relay Networks
## 1 Introduction
By incorporating hydrological factors into the evaluation of connectivity probability, this approach systematically analyzes communication systems in complex marine environments. The results demonstrate that optimizing relay selection strategies significantly enhances the system's adaptability to dynamic conditions and improves data transmission reliability.
# Dissimilar Rotary Friction Welding of Inconel 718 to F22 Using Inconel 625 Interlayer
## 2 Material Description and Experimental Procedures  
Base metal alloys, viz., Inconel 718, Inconel 625, and F22 cylindrical rods (20 mm \(\varphi\) and 100 mm length) were used in this study. The chemical composition of the base metals is shown in Table 1. Inconel 718 and Inconel 625 are in mill-annealed condition, while the F22 is quenched and tempered. Inconel 718 and Inconel 625 are in-process annealed at 1010 and 1050 \({}^{\circ}\)C, respectively, and subjected to rapid cooling. The heat treatment of F22 involves heating to a temperature of 1000 \({}^{\circ}\)C to fully dissolve carbides and quenching to room temperature. The quenched F22 steel is tempered for 2 h at 650 \({}^{\circ}\)C to reduce the martensite hardness and form stable carbides. The base metal rods were face-prepared by machining and acetone cleaning. Welding was carried out using a continuous drive rotary friction welding machine (ETA Technology, Bangalore, India) with a maximum axial force of 150 kN. The welding procedure corresponding to the rotary friction welding machine has been explained elsewhere [22]. Weld trials were carried out to optimise burn-off length, upset and friction pressures, and final friction welding parameters (Table 2). In order for the the welding parameters to be optimised, the friction pressure, burn-off length, and upset pressure were varied one parameter at a time. Lower friction pressures (below 200 MPa) resulted in longer weld times and poor flash appearance with flaky morphology, indicative of insufficient plasticisation. Once the friction pressure was frozen at 200 MPa, the best flash appearance was observed at 6 mm burn-off length and 400 MPa upset pressure. Visual flash inspection, burn-off length, and weld drop tests were employed for the optimisation of the welding parameters. The Inconel 718/F22 weld joints with Inconel 625 interface were developed in two stages. F22-Inconel 625 friction welds were prepared during the first stage of the experiments. The F22/Inconel 625 friction weld joints were cut on the Inconel 625 side at 15 mm away from the weld interface. After we removed the flash and face preparation, second stage welding was performed between Inconel 718 and Inconel 625/F22 rod to produce the Inconel 718/F22 friction weld joint with Inconel 625 interlayer. The schematic of the Inconel 718/F22 friction weld joint development with Inconel 625 as an interlayer is visualised in Figure 1. For comparison, Inconel 718/F22 friction weld joints without interlayer were produced in this study.  
-ray (EDS) spectrometer (TESCAN ORSAY HOLDING, Brno, Czech Republic). The line intercept method was employed for the determination of the average grain size of the base metal and thermo-mechanically affected zone (TMAZ). X-ray diffraction (PANalytical, Malvern, United Kingdom, X'pert powder XRD) technique using Cu-K\({}_{\alpha}\) radiation was used for the phase analysis of the base metals.
# Research on Path Planning Based on the Integrated Artificial Potential Field-Ant Colony Algorithm
## 4 ACA Integrated with the APF Method
### Improvement of the Pheromone Update Mechanism  
During the operation of the traditional ant colony algorithm, as the search cycle becomes longer and longer, the pheromone content in the search space gradually accumulates. This accumulation of pheromones causes subsequent ants participating in the path finding to be more inclined to choose routes with higher PCs. As pheromones evaporate, those paths less frequently chosen by ants may gradually lose pheromones, which can very easily enable the algorithm to find a local optimal solution. In the current study, for a specific problem, an adaptive pheromone regulation strategy was designed. This strategy is based on the initial pheromone increment update mechanism. On this basis, the pheromone constant values carried by all ants on the optimal path in the previous generation's optimization process are summarized and the pheromone constant values carried by all ants on the worst path in the same process are subtracted. By using this method, a new, self-adaptive pheromone update mode was successfully constructed, and the specific mathematical expressions are shown in Formulas (18)-(20).  
\[\tau^{\prime}_{ij}(t+1)=(1-\rho)\tau_{ij}(t)+\sum_{k=1}^{m}\Delta \tau_{ij}^{k}+\Delta\tau_{ij}^{g}(t)-\Delta\tau_{ij}^{w}(t) \tag{18}\]  
\[\Delta\tau_{ij}^{g}(t)=\left\{\begin{array}{l}\frac{b\times Q}{L_{min}},j\in T ^{g}\\ 0,\text{Others}\end{array}\right. \tag{19}\]  
\[\Delta\tau_{ij}^{w}(t)=\left\{\begin{array}{l}\frac{w\times Q}{L_{max}},j\in T ^{w}\\ 0,\text{Others}\end{array}\right. \tag{20}\]  
In Equations (18)-(20), \(\Delta_{ij}^{g}(t)\) represents the increase in PC on the optimal path between node \(i\) and node \(j\) at time \(t\); \(\Delta_{ij}^{w}(t)\) represents the increase in PC on the worst path between node \(i\) and node \(j\) at time \(t\); \(b\) represents the number of ants that successfully find the optimal route; \(w\) corresponds to the number of individuals trapped in the inefficient path; and \(L_{min}\) and \(L_{max}\) record the extreme parameters of the planned path, respectively--the former represents the shortest feasible distance confirmed by the system, and the latter indicates the maximum detour length found in actual exploration.
## 3 Results and Discussion
### Beam Steering
Figure 5: (a) Radiation patterns of patch array in the \(x\)-\(z\) plane with phase gradients of \(-6\pi\)/7 (green line), \(-4\pi\)/7 (blue line), \(-2\pi\)/7 (red line), 0 (black line), \(2\pi\)/7 (pink line), \(4\pi\)/7 (olive green line), and \(6\pi\)/7 (navy line) and (b) the corresponding electric field distribution in the \(x\)-\(z\) plane when the beam steering angle was \(-55^{\circ}\), \(-34^{\circ}\), \(-17^{\circ}\), \(0^{\circ}\), \(17^{\circ}\), \(34^{\circ}\), and \(55^{\circ}\), respectively.
## A Network Coding Based Routing Protocol for Underwater Sensor Networks  
**[PERSON] 1, [PERSON] 2,* and [PERSON] 1**  
1 School of Information Science and Technology, Heilongjiang University, Harbin 150080, China; E-Mails: [EMAIL_ADDRESS] (H.W.); [EMAIL_ADDRESS] (X.G.)  
2 School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS].  
_Received: 15 February 2012; in revised form: 14 March 2012 / Accepted: 22 March 2012 / Published: 10 April 2012_  
**Abstract:** Due to the particularities of the underwater environment, some negative factors will seriously interfere with data transmission rates, reliability of data communication, communication range, and network throughput and energy consumption of underwater sensor networks (UWSNs). Thus, full consideration of node energy savings, while maintaining a quick, correct and effective data transmission, extending the network life cycle are essential when routing protocols for underwater sensor networks are studied. In this paper, we have proposed a novel routing algorithm for UWSNs. To increase energy consumption efficiency and extend network lifetime, we propose a time-slot based routing algorithm (TSR).We designed a probability balanced mechanism and applied it to TSR. The theory of network coding is introduced to TSBR to meet the requirement of further reducing node energy consumption and extending network lifetime. Hence, time-slot based balanced network coding (TSBNC) comes into being. We evaluated the proposed time-slot based balancing routing algorithm and compared it with other classical underwater routing protocols. The simulation results show that the proposed protocol can reduce the probability of node conflicts, shorten the process of routing construction, balance energy consumption of each node and effectively prolong the network lifetime.  
**Keywords:** underwater sensor network; time-slot based routing algorithm; balance degree; network coding; change probability
## 3 Order Tracking Based on the Proposed GAD Method
### The Main Procedure of the GAD Method
#### 3.1.1. Linear-Phase Filter for Gravity Acceleration Component Detection  
In order to detect the time-varying gravity acceleration component and ensure that the waveform does not change, a linear-phase FIR low-pass filter is designed based on the window function [25]. Firstly, the desired amplitude-frequency characteristics are given as  
\[H_{d}(e^{i\omega})=\left\{\begin{array}{ll}e^{-i\omega\alpha}&(|\omega|\leq \omega_{c})\\ 0&(\omega_{c}<|\omega|<\pi)\end{array}\right. \tag{3}\]  
where \(\alpha\) denotes the time delay. Then, the desired unit pulse responses with a linear-phase can be obtained as  
\[h_{d}(n)=\frac{1}{2\pi}\int_{-\pi}^{\pi}H_{d}\Big{(}e^{i\omega}\Big{)}e^{i \omega n}d\omega=\frac{1}{2\pi}\int_{-\pi}^{\pi}e^{i\omega\alpha}e^{i\omega n }d\omega=\frac{\sin[\omega_{c}(n-\alpha)]}{\pi(n-\alpha)} \tag{4}\]  
\[\alpha=\frac{N-1}{2} \tag{5}\]  
Then, the window function is adopted to cut off \(h_{d}\) to obtain a finite length sequence:  
\[h(n)=h_{d}(n)w_{N}(n)\quad(n=0,1,\ldots,N-1) \tag{6}\]  
\[N=\frac{2\pi A}{\Delta\omega} \tag{7}\]  
where \(w_{N}\) denotes the window function, \(N\) refers to the filter length, \(A\) means the constant related to the window function and \(\Delta\omega\) represents the transition band width between the passband and stopband.  
The band containing the gravity acceleration component can be calculated based on the derived filter, as follows:  
\[S_{g}(t)=h*Sig(t) \tag{8}\]  
where \(Sig(t)\) represents the acquired vibration signal of the local in situ accelerometer. And it is known that there is a linear-phase delay after FIR filtering, so a phase compensation step is applied, in which the phase delay in the design filter is \(\alpha\), and the compensated signal is marked as \(S_{gc}\).
# An Internet of Things-Based Low-Power Integrated Beekeeping Safety and Conditions Monitoring System
## 4 Proposed IBSMC System Experimentation  
IBSMC system experimentation is divided into two separate testing scenarios: Scenario I includes the author's protocols experimentation, focusing on the RF12B BSDP protocol and the over LoRaWAN forwarding BSDB frame. The BSDB protocol is responsible for the periodic collection of the beehive cells conditions array via RF the central beehive. Then, LoRaWAN infrastructure is used for the forwarding of messages to the ISMCA LoRa server via the gateways. In Scenario I, the authors examine IBSMC frame losses and mean RTT via RF and LoRaWAN forwarding frames separately. In Scenario II, authors examine the energy expenditure of their proposed actuator systems: A) Peltier cells actuator,and B) Thermopad cells actuator, in separate critical environmental conditions (high temperatures of 36 \({}^{\circ}\)C and low temperatures of 0 \({}^{\circ}\)C) and measure their proposed actuators offered temperature difference achieved from the environment and the energy required for those actuators to operate.  
For LoRaWAN application protocols, the scenario includes the central hive communicating to the ISMCA service, publishing messages over MQTT via a LoRAWAN gateway that includes an IMST iC880A concentrator board connected via SPI to a RPIv2 board. The gateway is placed in three discrete LOS distances from the central hive (1 Km, 5 Km and 8 Km) bypassing the things network fair access policy of 0.37% and moving via the custom LoRa server to the EU regulation of 1% duty cycle use for OTAA devices (in the 868 MHz ISM band) and receiving frames from the LoRa server once per two node transmissions (maximum expected).
###### Abstract  
Nowadays, regarding high percentage of wind power penetration in power systems, operating conditions of power system necessitates wind turbine generators (WTGs) to contribute in frequency regulation of the system, similar to the conventional units. To reach this goal, active power output of WTGs must be controlled, but this issue distances the operating conditions of WTGs from maximum power point tracking (MPPT) mode. This paper initially presents a completed model of doubly fed induction generator (DFIG) for dynamic studies in frequency stability analysis. Next, a coordinated control strategy to regulate active power command set point (Pcmd) for individual WTGs in a wind farm (WF) and a control strategy to regulate wind power output of DFIG upon operator's request is presented. Stability is assured under different wind conditions in the proposed control strategy. Individual WTGs set point allocation is performed applying fuzzy logic controller (FLC), while emotional learning based intelligent controller is used in regulation of the coefficient in the DFIG to reach the best stability conditions. Simulation results, performed on a test system consisting of both conventional units and WTGs, validate the effectiveness of the proposed control strategy in comparison with other mentioned solutions.  
emotional learning based intelligent controller; frequency stability; fuzzy logic controller (FLC); wind power; wind turbine generator (WTG) +
Footnote †: journal: Applied Sciences
## 4 AI-Enabled Intelligent 6G Network Applications
### Autonomous-Vehicle Communications  
While varying degrees of autonomy are now being tested in a number of countries across the world, mass-produced autonomous cars appear to be a long way off. Sixth-generation (6G) communications have several applications and are gaining appeal as a novel strategy to integrate current automobiles and communication technologies in autonomous vehicles (AVs). Communication between autonomous vehicles and infrastructure systems will be more precise or better in real time with 6G networks. Because of the functional efficacy of communication activities, the role of artificial intelligence in 6G networks expressly adds to real-time routing decisions, notably in autonomous-vehicle navigation [21; 22].
# Smart Grid Testing Management Platform (SGTMP) +
## 3 Implementing SGTMP
### Test Execution and Result Processing
#### 3.4.7 Logging  
A test run can run for hours or even days. It is a requirement to have more information about a test run than only a simple pass/fail result. Certain conditions may be hard to re-create reliably (combination of several factors) or too time-consuming to run again (e.g., fault manifests only after running a simulation for multiple days), or equipment might get damaged in the process, and the technician must determine the exact cause of this failure from only the data which survived. For these purposes, a logging framework was added to SGTMP. There are three parts of the logging framework: test measurement logging, simulator local logging, and Mosaik logging.  
Logging of measurements is done by the global test evaluator, which receives all of the tracked measures for each step and is responsible for evaluating whether they are within the specified range for each step, as specified by the relationship between test step and the expected result. In addition to evaluating measures, the component also logs them. These measures are then saved to the database for future analysis, which can be done for each test step. By allowing the users to view all of the measures during a test run, it is possible to determine the state of each testing component for any given step in time.  
Each simulator is responsible for logging additional non-measure-related information. If the simulator was launched by SGTMP, the log file will be directly accessible to the user. Simulators started and managed outside of SGTMP must handle their log file.  
The final logging component is from the Mosaik framework itself. If a simulation unexpectedly crashes or does not finish, this should be the first log examined. It should contain information regarding which simulators crashed, whether there were any configuration errors or whether Mosaik process itself crashed. This log is available immediately after the Mosaik process quits.
## 2 Research Methodology
### ELM Model  
The Extreme Learning Machine (ELM) Model is a type of machine learning algorithm based on neural networks. Unlike traditional neural network models, ELM initializes the weight of the hidden layer randomly and calculates the weight of the output layer with only one training iteration. This characteristic allows ELM to provide fast learning speeds and a good generalization performance [13]. The structure of an ELM model consists of an input layer, a hidden layer, and an output layer. The input layer receives the given data, and the hidden layer, located between the input and output layers, calculates the output values of hidden neurons. The neurons in the hidden layer perform a nonlinear transformation on the input data, and the output layer calculates the final output based on the output values of the hidden layer. The basic concept of an ELM model is as follows: There is a hidden layer between the input and output layers, that has randomly initialized weights. The input data is passed to the hidden layer, and the neurons in the hidden layer calculate the output values through an activation function.  
The output value \(H\) of the hidden layer is calculated using the input data \(X\), the weights \(W_{1}\), and the bias \(b_{1}\), as shown in Equation (11).  
\[H=g(X\cdot W_{1}+b_{1}) \tag{11}\]  
here, \(g\) represents the activation function of the hidden layer. After calculating the output of the hidden layer using the input data, an ELM model computes the weights of the output layer by solving a system of linear equations. The calculation of the output layer weights is given by Equation (12). The output layer weights, \(W_{2}\), are calculated using the output values \(H\) of the hidden layer and the actual output values \(Y\).  
\[W_{2}=(H^{T}\cdot H+C)^{-1}\cdot H^{T}\cdot Y \tag{12}\]  
here, \(H^{T}\) represents the transposition of the hidden layer output values \(H\), and \(C\) is a term for regularization. The weights can be obtained using methods such as Least Squares or Pseudo-inverse. With the computed weights of the output layer, predictions for new input data can be performed. The prediction for new input data is given by Equation (13), where the new input data, \(X\_new\), is used along with the weights, \(W_{1}\), and bias, \(b_{1}\), of the hidden layer to calculate the output values, \(H\_new\), of the hidden layer. The predicted value, \(Y\_pred\), is then calculated using the weights, \(W_{2}\), of the output layer.  
\[H\_new=g(X\_new\cdot W_{1}+b_{1})\ Y\_pred=H\_new\cdot W_{2} \tag{13}\]  
ELM offers fast learning speeds and a good generalization ability compared to traditional neural network algorithms. Additionally, ELM initializes the weights of the hidden layer randomly, reducing sensitivity to initial weight settings and eliminating the need for manual weight adjustment by users. Due to these characteristics, ELM can be effectively used in various fields, particularly for handling large-scale data and real-time predictions.