esa-sceva/satcom-chunk-collection huggingface dataset:
# Review and Comparison of Emerging Routing Protocols in Flying Ad Hoc Networks
## 2 Differences between FANETs and Traditional Ad Hoc Networks
**F. Power consumption and network life**  
Developing energy-efficient routing is a major part of increasing network life. However, FANET communication consumes UAV's energy, which means that FANET communication hardware does not have the same power resource problems as MANET. In this case, unlike most MANET applications, the FANET designing in applications may not be power-sensitive. However, we have to pointed out that power consumption is still a designing problem of a micro UAV [29]. **G. Computing power**  
MANET nodes are small, battery-powered computers such as laptops, PDAs, and smartphones. Due to the limitation of node size and energy, the computing capacity of node is limited. On the other hand, VANET and FANET support devices with high computing power. **H. Positioning**  
In MANET, GPS is used to receive the coordinates of a terminal device, and in general, it is enough to obtain the location of a node. In VANET, the accuracy of navigation level GPS receiver is approximately 10-15 m, so it can accept trajectory guidance. Due to the high speed of nodes and dynamic topology of multi-UAVs network, FANET requires more real-time and accurate positioning data. Compared with other types of ad hoc networks, FANET shows quite dynamic characteristics due to the inherent differences in terms of functionalities. Because of these unique characteristics, FANET is regarded as a kind of relatively independent ad hoc network and has attracted many researchers.
###### Abstract  
A flame retardant polyol (EP-DOPO) with epoxy functional groups was synthesized by reacting a 1,6-hexanediol glycidyl ether with a flame retardant 10-(2,5-dihydroxyphenyl)-10\(H\)-9-oxa-10-phospha-phenanthrene-10-oxide (DOPO). The polyurethane (EPPU) with enhanced heat resistance was prepared by the reaction of a polyol blend of EP-DOPO and polytetrahydrofuran (PolyTHF) at a ratio of 1:1 with isophorone diisocyanate. EPPU useful for the preparation of cables or coatings showed higher thermal decomposition temperature rather than that of reference polyurethane synthesized by the reaction between pure PolyTHF and isophorone diisocyanate by thermogravimetric analysis. Further study of the polyurethane as a toughening agent for epoxy polymers was carried out. Epoxy compositions consisting of bisphenol A epoxy resin and dicyandiamide as a hardener have a brittle property allowing crack propagation after cure. Polyurethane plays an important role as an impact modifier to prevent from cracks of epoxy polymers. Various contents of EPPU were added into epoxy compositions to measure the physical property changes of epoxy polymers. The tensile and flexural strengths of the cured specimen were compared with those of epoxy compositions including reference polyurethane. Furthermore, the crosslink density of the cured epoxy compositions was compared.  
polyurethane; flame retardant; epoxy; crosslink density; impact resistance 1
## 4 Improved Kernel Extreme Learning Machine  
### Brief Outline of KELM  
Assume that \(x_{p}\in\mathbb{R}^{n}\) are the original data, \(t_{p}\in\mathbb{R}^{m}\) is the corresponding target output, \(T\) is the target output set, the number of hidden layer neurons is \(l\), \(\beta_{i}\) is the weight of the connection betweenthe \(i\)-th hidden layer neuron and the output layer, and \(h(x_{p})\) is the activation function that maps data from the input layer to the \(i\)-th hidden layer neuron. Then, the ELM output is as follows:  
\[f(x_{p})=\sum\limits_{i=1}^{l}\beta_{i}h_{i}(x_{p})=h(x_{p})\beta \tag{19}\]  
The ELM training goal is defined as follows:  
\[\text{Minimize}:\ L_{P_{ELM}}=\frac{1}{2}\beta^{2}+C\frac{1}{2}\sum\limits_{p =1}^{N}\xi_{p}^{2} \tag{20}\]  
\[\text{Subject to}:\ f(x_{p})=h(x_{p})\beta=t_{p}^{T}-\xi_{p}^{T},p=1,\cdots,N \tag{21}\]  
where the first part of \(L_{P_{ELM}}\) is the structural risk, and the second part is the empirical risk; \(C\) is a penalty coefficient; \(t_{p}\) is the theoretical output; and \(\xi_{p}\) is the error of \(t_{p}\) versus \(f(x_{p})\).  
To solve the above optimization problem, a Lagrange function is defined as follows, where \(\alpha\) is a Lagrange factor:  
\[L_{P_{ELM}}=\frac{1}{2}\beta^{2}+C\frac{1}{2}\sum\limits_{p=1}^{N}\xi_{p}^{2}- \sum\limits_{p=1}^{N}\alpha_{p}(h(x_{p})\beta-t_{p}-\xi_{p}) \tag{22}\]  
Based on Karush-Kuhn-Tucker (KKT) theory, the solution for the above equation is as follows:  
\[\left\{\begin{array}{l}\frac{\partial L_{P_{ELM}}}{\partial\beta}=0\to\beta= \sum\limits_{p=1}^{N}\alpha_{p}h(x_{p})^{T}\to\beta=H^{T}\alpha\\ \frac{\partial L_{P_{ELM}}}{\partial\xi_{p}^{T}}=0\to\alpha_{p}=C\xi_{p},p=1, \cdots,N\\ \frac{\partial L_{P_{ELM}}}{\partial\alpha_{p}}=0\to h(x_{p})\beta-t_{p}^{T}+ \xi_{p}^{T}=0,p=1,\cdots,N\end{array}\right. \tag{23}\]  
It follows that  
\[\beta=H^{T}(\frac{I}{C}+HH^{T})^{-1}T \tag{24}\]  
where \(T=[t_{1},t_{2}\ \cdots\cdots\ t_{N}]^{T}\) is the target vector of the input sample.  
The hidden layer output matrix \(H\) consists of randomly generated weights of connections between the input layer and hidden layer and thresholds of hidden layer neurons. In essence, it is a random mapping. Owing to such randomness, a different \(H\) is generated each time. Therefore, the calculated \(\beta\) is different, which leads to fluctuations in the ELM output and inferior stability and generalization capability [24]. [PERSON] et al. replaced \(HH^{T}\) with a kernel function \(K(x_{i},x_{j})\) to obtain the KELM algorithm, which prevents random assignment-induced fluctuations in the results of KLM:  
\[HH^{T}=\Omega_{ELM}=\left[\begin{array}{ccc}K(x_{1},x_{1})&\cdots&K(x_{1},x _{N})\\ \vdots&\ddots&\cdots\\ K(x_{N},x_{1})&\cdots&K(x_{N},x_{N})\end{array}\right] \tag{25}\]  
Equations (24) and (25) are substituted into Equation (18) to obtain the KELM output:  
\[f(x_{p})=\left[\begin{array}{c}K(x,x_{1})\\ \vdots\\ K(x,x_{N})\end{array}\right]^{T}(\frac{I}{C}+\Omega_{ELM})^{-1}T \tag{26}\]A radial basis function (RBF) is used as the kernel function:  
\[K(x,y)=\exp(-\frac{\|x-y\|^{2}}{2\sigma^{2}}) \tag{27}\]
## 5 Experimental Result
### Execution Time vs. Activity-Factors
Figure 10: (**A**) Cycle count for the event-driven eLSNN for the best configurations (input number with different colours, and inference ticks with different markers) computed on minimal, median and maximal spike activities on the input-layer (\(x\)). (**B**) Cycle count for the current-driven eLSNN computed on minimal, median and maximal spike activities on the recurrent/hidden-layer (\(z\)). Different colour represent different number of inputs configuration for the eLSNN. All networks have an 10 inference ticks. Figure 10A reports for each of the different configurations of the first-order hyper-parameters and for the input sample corresponding to the minimum, maximum and median values of \(x\) the execution time of the event-driven eLSNN. We can see that both the first-order hyper-parameters vary the execution time. The execution time increases linearly with the inference ticks and with \(x\). It is interesting to notice that for the event-driven eLSNNs, the input number (\(I\)) does not increase the execution time directly as with the current-driven eLSNNs but indirectly. Indeed, a larger input number (\(I\)) increases the execution time proportionally to the increase of \(x\). Which eLSNN flavour and configuration should thus be preferred?
## 3 Types of Underwater Communication Systems
### Underwater Wireless Communication
This approach is inspired by the natural communication methods of some species of fish, such as the African Mormyridae and the South American Gymnoti-formes [19], which possess electroreceptor organs and generate electric organ discharges (EOD) [75]. Some electric fish species use weak EF for both communication and navigation. By mimicking this principle, researchers are developing systems that transmit data through EF in the water [75] known as UEFC. A software-based simulation by [PERSON] et al. [75] demonstrated a transmission link of 2 megabits per second over several meters using digital modulation in the 2 MHz frequency range. The proposed communication method aims to address the low range associated with attenuation, which is particularly relevant when large amounts of data are required, as seen in optical communication [75]. [PERSON] et al. [95] reported a communication distance of 3.8 m using Binary Frequency Shift Keying (2 FSK) modulation in the implementation of UEFC. When spread spectrum modulation was implemented, the transmission distance rose up to 8 m at a 1.2 Kbps data transmission rate [95]. However, when Binary Amplitude Shift Keying (2 ASK) modulation was used, the transmission range was only 2.4 m [95]. Continuous research on UEFC is ongoing. With current advances in AI, various ML classification methods are now widely used to extract valuable information through the application of SONAR [2; 96]. [PERSON] et al. [2] covered several studies that have successfully implemented various ML models to classify bioacoustic signal types, achieving a classification accuracy of 96.9% using Support Vector Machine (SVM) and random forest [2]. The results demonstrate the feasibility of underwater acoustic classification for monitoring and surveillance purposes [2]. In [2], bioacoustics were classified based on fish behavioural sound types, such as impulsion, drums, roars, and quacks, referred to as positive classes. Negative classes were designated as Background and Unknown [2]. In [28], the study included both anthropogenic and bioacoustic signals, comprising 102 sounds. The two studies in sound types confirm that ML models do effectively classify underwater bioacoustic signals. In addition, other AI models have been successfully employed to automatically classify sound sources [97]. With more advanced methods, like Spiking Neural Network (SNN) inspired by brain and auditory systems, AI is gaining momentum for processing sounds emitted by marine life [97]. Before the adoption of ML and DL techniques for signal processing, traditional methods dominated the field. These methods, which include DSP and filtering techniques, are still used for identifying sound sources [98]. For instance, ref. [98] used DSP to recognise broadband hydroacoustic signals, with the signal being digitised via a microcontroller at 70 kHz with 12-bit resolution. By averaging the signal power in a sliding window and comparing it to a dynamic threshold, detection was achieved.
# Prediction Methods and Experimental Techniques for Chatter Avoidance in Turning Systems: A Review
## 5 Practical Solutions for Industry
In this case, the chatter avoidance system uses the value of vibration captured by a sensor (amplitude and frequency) and other parameters (number of cutting edges, range of allowed speeds, threshold values for detection) to modify the spindle speed and the feed-axis for a more stable cut. The program can work on its own or be activated/deactivated by the operator and allows them to apply the speed before detecting chatter onset. Makino: Makino Milling Machine (Tokyo, JP) patented a method [119] for chatter avoidance in machining systems (US 9.285,797 B2). This method was based on acquiring vibration data of the tool at a predetermined sampling period, then deciding as fast as possible whether the vibration data being processed matches an integral multiple of the calculated period or not. Blue Swarf(c) [120; 121] is a company that emerged from the expertise of Dr. [PERSON] (University of North Carolina at Charlotte), Dr. [PERSON] (President of Manufacturing Laboratories Inc. MLI), Mr.  
[PERSON] (Founder and President of American Machinist), and Dr. [PERSON] (University of North Carolina at Charlotte.). This software offers patches to solve vibrations problems in machining. Customers make use of individual dashboards for the identification of a particular machine tool-spindle-tool + material assembly. As a result, Blue Swarf makes a recommendation on the best cutting parameters. However, the software is more oriented to solve problems for milling rather than turning. ChatterMaster [122; 123]: from the University of Tarbes (ENIT), it follows the same approach by [PERSON]. [PERSON] or MLI with Harmonizer. After extracting data of the machining system (spindle-tool material and cutting conditions) the software gives a recommendation for the best spindle speed to avoid chatter. Recently, ChatterMaster has developed a new module for chatter avoidance in turning operations.
## 2 Methods
### Vehicle Emission Rate of Particle Size Distributions  
In the present study, the numbers of vehicles traveling in the tunnel, distinguished by the types of vehicles, were counted hourly for the analyses of number concentration, particle size distribution, and vehicle emission rate. The measurement of air pollutants obtained in the comparatively closed space of tunnel was expected to remove the effects of diffusion of exhaust from vehicles and ordinary atmosphere. The measurements of varied number concentration and particle size distribution at every hour, together with numbers and types of vehicles, enabled the analyses of the relationship of number concentration and particle size distribution with vehicles of each type. Equation (1) represents the calculation of vehicle emission rate based on the measurement of traveling vehicles in the tunnel [28].  
\[\text{Vehicle emission rate (N/veh\cdot km)}=\Sigma(\text{C}_{\text{in}}-\text{C}_{ \text{out}})\times\text{A}\times\text{U}\times\text{t/[\#\times L]} \tag{1}\]  
\[\text{Vehicle emission rate (N/veh\cdot km)}=\Delta\mathbb{C}\times\delta \tag{2}\]  
Here, \(\text{C}_{\text{in}}\) and \(\text{C}_{\text{out}}\) represent the number concentration (N/m\({}^{3}\)) measured inside and outside of the tunnel, while A represents the area of cross section of the tunnel (m\({}^{2}\)) (69.33 m\({}^{2}\)). U denotes the wind speed inside of the tunnel (m/s), and t, N, and L represent the reference time (second) for analysis, number of vehicles (#) passed through the tunnel while exhausts therefrom were collected, and the length of the tunnel (m) (17,000 m), respectively. From equations above, the number concentration from vehicles free from effects of other external sources of contamination were calculated, and then the vehicle emission rates were calculated from the number of vehicles passed through the unit length of the tunnel, to distinguish the difference between number concentrations discharged from vehicles inside and outside of the tunnel. Equation (1) can be simplified as the expression in Equation (2). Here, \(\Delta\mathbb{C}\) represents the difference in particle number concentration between inside and outside of the tunnel and the tunnel flow coefficient (TFC, \(\delta\)) (m\({}^{3}\)/(veh\cdot km)) can be defined as \"A \(\times\) U \(\times\) t/[#\(\times\)L].\" TFC enables to calculate the vehicle emission rate of respective chemical compounds from the difference in concentration (\(\Delta\mathbb{C}\)) of other air pollutants, including the number concentration. Thus, the TFC can be explained as the volume of air that is polluted by one car driving one km. Besides, together with the TFC, the speed of vehicles passing through the tunnel, as well as characteristics of drivers of each vehicle, can be taken into account to analyze the relationship with vehicle emission rate.
# Microstructural, Electrochemical and In Vitro Analysis of Mg-0.5 Ca-xGd Biodegradable Alloys
## 3 Results and Discussions
### Cytocompatibility Study
As other authors have indicated, Mg alloy degradation events may harmfully affect the cell metabolism through ion bulk leakage from Mg alloys and subsequent precipitation of salts with an inhibitory or toxic influence, and by increasing of the osmolarity (as a result of alloy reactivity) which may lead to hyperosmotic shock [54]. It should be point out that because of Mg alloys' degradation depend on the microenvironment conditions [55], different corrosion/degradation events under particular biomedical scenarios (i.e., different flow and/or composition of biological fluids) can take place by affecting the surface chemistry of the implants. These facts are still to be proved by an ongoing in vivo study. Furthermore, the level of viability recorded at 3 days was 73-80% and, at 5 days, was 77-100%, which permits us to consider (according to the ISO 10993-5 standard [56]) that the experimental samples do not appear to exhibit a long-term cytotoxic character. This is due to the continuous exposition of the cells to the sum of the abovementioned occurrences that occurred during the biodegradation of Mg alloys (i.e., exposure to 100% extracts for up to 5 days). The registered viability profiles correlate with the results on the density and morphology of fibroblasts (Figure 8) co-incubated with the experimental samples for 24 h and 5 days, respectively. It has been shown that after 24 h, the cell density in the case of the experimental alloys was lower than in the case of the control wells (i.e., negative control, containing only cultured cells without the Mg alloy sample). This finding is in agreement with the cell viability results, being confirmed by the data obtained after 5 days of co-incubation of the cells with the experimental alloys. In addition, different cell spreading was observed inside the wells, such as high density areas containing cells having an elongated appearance and a bipolar morphology, and low density areas (observed towards the center of the wells and around the inserted membrane), where the cells had a polygonal shape with extensive lamellipodia-like cytoplasmic processes. The cells' variable morphology maybe associated with the regional micro-environmental changes inside the wells concerning the Ca, Mg and Gd ionic concentrations (as a result of the degradation of Mg alloys inside the insert), considering the associated structural role of calcium in the reorganization of cytoskeleton constituents [57], while magnesium accumulated up to a critical concentration could have an inhibitory effect [58]. Figure 7: Cell viability vs. studied Mg–0.5 Ca–xGd samples: Effect of Gd alloying on cell viability after 1, 3, and 5 days of co-incubation. Results are shown as percent of the control wells’ viability (i.e., negative control). (\(\vee\), \(\#\)) Not statistically significant (\(p>0.05\)); (*) statistically significant (\(p<0.05\)).
# Extended L-Band InAs/InP Quantum-Dash Laser in Millimeter-Wave Applications
## 2 Experimental Setup
### MMW Generation Setup
The single locked mode was then fed to a 98/2 CP-2, from which 2% of power was continuously monitored by an optical spectrum analyzer (OSA, Agilent 86142B) at a resolution bandwidth (RBW) of 60 nm, while 98% of power was fed to a dual-wavelength generator (DWG). The output of the generator was a coherent dual-wavelength laser emission with a 33-GHz free spectral range (FSR), as depicted in the inset of Figure 2b (location P2), exhibiting an optical signal-to-noise ratio (OSNR) of ~36 dB. This was accomplished by PC-2 by phase-modulating the single SIL mode with a 33 GHz radio-frequency signal in a phase modulator (PM), optically filtering it via TBPF-2 (XTM-50 EXFO) and amplifying it through EDFA-2 (AEDFA-L-EX2-B-FA). The dual wavelength was then beaten in a 70-GHz photodetector (Finisar XPDV3120), and the electrically generated 33 GHz MMW carrier, analyzed using Keysight N9010B electrical signal analyzer (ESA), is shown in Figure 2b. It is noted that with efficient optical coupling into the SMF, EDFA-1 could be eliminated. Besides, a customized tunable fiber Bragg gratings could replace TBPF1. This made the setup compact and power-efficient. Moreover, the QD-DWL (Figure 1) could be replaced by a single QD-LD device and mode-locking should observed in L-band devices, as already demonstrated in 1550 nm devices, to ascertain the coherency of the modes. Unfortunately, owing to the non-optimized device design and the challenges pertaining to components and equipment operating wavelength limitations, mode-locking has not been observed yet in the mid-L-band region, although it is comparatively straightforward to investigate in C-band QD-LDs for MMW applications. Nevertheless, this work paves the way for deploying QD-LDs as potential MMW sources across the entire C- and L-band regions for operation, and together with the realization of passive mode-locking in the future, are promising to serve as a single MMW source for RoF-WDM networks by exploiting the coherent multiwavelength lasing feature, as has been demonstrated in optical WDM system [27].
# A Phase Bias Compensation Method for Anti-Interference Antenna Arrays in RTK Positioning
## 1 Introduction
The second step processes the anti-interference output signal, which has low hardware complexity. Therefore, the proposed algorithm does not require the direction of the incident signal and has low hardware complexity. The structure of paper is as follows. In Section 2, the mathematical model of antenna array processing and RTK positioning are introduced. In Section 3, the characteristics of carrier phase bias are analyzed. Moreover, we introduce the algorithm. In Section 4, we simulate the anti-interference performance and discuss the phase bias compensation results.
# A Review on Rainfall Measurement Based on Commercial Microwave Links in Wireless Cellular Networks
## 6 Monitoring Phenomena Other than Rain
### Hydrometeor Types  
[PERSON] et al. [101] proposed a method to identify rain, graupel, and wet snow based on microwave links, and single-frequency, dual-frequency, and triple-frequency models are established by using the extreme learning machine algorithm to analyze the hydrometeor size distribution data in Nanjing. The results indicate that accuracy increases as the total frequency or frequency difference among microwave links increases.  
When using CML to measure rainfall in stormy weather, the wind will cause the antenna to shift slightly, resulting in the RSL fluctuations. Atmospheric dust particles and vehicle emissions (e.g., CO and NO\({}_{x}\)) can also induce microwave signal attenuation and phase shift. Hence, it seems feasible to monitor wind, dust and air pollution by measuring the attenuation or phase change caused by these small changes.