esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 System Architecture
### FSK Transmission  
A voltage-controlled oscillator (VCO) is integrated as the last stage of our system to act as a binary FM transmitter and to generate an FSK signal. The resonant circuit consists of a 26 nH planar inductor and two accumulation-mode varactors that serve as the tuning elements. In order to minimize the bandwidth of transmission, the tuning voltage is reduced to 200 mV from the 2.5 V digital signal using a resistive divider. We presented the details of this circuit in [20] where it was used as an analog FM transmitter instead, i.e., the recorded signal modulated the varactors directly. In this application, we connected the serial data stream generated by the microcontroller to create the FSK signal. A predefined header byte precedes each byte of the data to help with the synchronization at the receiver.  
On the receiver side, we used a similar setup as the one described in [20], connecting a dipole antenna to an advanced SDR-based receiver (AirSpy R2 by AirSpy Networks, Inc., Paris, IDF, France) plugged into a Windows(r)laptop running an open-source software development kit, GNU Radio. Figure 6 shows the flow graph of our custom FSK receiver developed using the GNU Radio Companion (GRC) tool. The first block is the data source representing the USB dongle with a complex output containing the baseband representation of the received FSK signal. This signal is passed through an LPF to limit its bandwidth to 1 MHz and fed into an FM demodulator with a frequency deviation that matches this limited bandwidth. The output is, then, passed through another LPF with a cutoff frequency of 10 kHz to extract our band of interest and reduce the noise. We used various time scopes and fast Fourier transform (FFT) sinks in the system to check the signal at different stages of the receiver, and a file sink to save the incoming stream of data so we can post-process it in MATLAB(r). This binary file contains the raw time-domain signal at 50 ksps. In order to extract the actual 8-bit samples from the ADC, we first need to apply a binary slicer and obtain a sequence of binary symbols. Then, the actual samples can be extracted by recognizing the predefined header that we added to the data frame. Finally, we convert the digital samples to an analog scale to represent the voltage levels of the recorded PPG waveform.  
Figure 5: (**a**) The control signals generated by the microcontroller repeating every 5.3 ms. The inset shows SI_RST (blue) and SI_EN (orange) used for operating switches S2 and S1, respectively. (**b**) Measured output of the switched integrator showing the instant when switch S2 resets the feedback capacitor and switch S1 enables the integration of the photocurrent immediately after that.
# Scan Matching by Cross-Correlation and Differential Evolution
## 5 Experimental Evaluation
### Experimental Methodology  
In each experiment, the DE was used to search for parameters of transformation \(\mathcal{T}\) that would align selected pairs of laser scans from the data set created using the simulation framework. 20 random laser range scans were selected from a single robot trajectory and matched with subsequent scans taken 5, 10, 20, 30, 50, 100 and 200 LiDAR cycles later. That roughly corresponded to traveled distances of 75, 150, 300, 450, 750, 1500 and 3000 mm. In total, 140 distinct scans were matched during the experiments.  
The experiments used the traditional/DE/rand/1 variant of the differential evolution with cross-correlation defined according to Section 3.4 as the fitness function. The DE worked with a population of 3-dimensional vectors representing the transformation parameters, \(t_{x}\), \(t_{y}\) and \(\Phi\). The remaining DE parameters were set after a series of trial-and-error runs and with respect to best practices and past experience. The size of the population, \(M\), was set to 100, the crossover probability, \(C\) and the scaling factor, \(F\), were both set to 0.9. The evolution was terminated after the maximum of 5000 generations and all experiments were independently executed 50 times to avoid bias caused the stochastic nature of the algorithm.
## 4 Experimental Results
### Tensile Tests of Unconditioned and Aged Specimens
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Specimen** & \begin{tabular}{c} **Young’s Modulus** \\ **E (GPa)** \\ \end{tabular} & \begin{tabular}{c} **Ultimate Load** \\ **Pu(kN)** \\ \end{tabular} & \begin{tabular}{c} **Tensile Strength** \\ **\(\sigma_{\text{ult}}\) (MPa)** \\ \end{tabular} & \
\begin{tabular}{c} **Elongation at** \\ **Failure (\%)** \\ \end{tabular} &
\begin{tabular}{c} **Cross Section** \\ **(mm2)** \\ \end{tabular} \\ \hline
1 AG & 30.39 & 4.20 & 454.33 & 1.5 & 9.2 \\
2 AG & 30.62 & 4.81 & 521.00 & 1.8 & 9.2 \\
3 AG & 29.54 & 3.37 & 364.61 & 1.2 & 9.2 \\
4 AG & 27.63 & 2.79 & 302.38 & 1.2 & 9.2 \\
5 AG & 30.77 & 3.44 & 372.51 & 2.2 & 9.2 \\
6 AG & 30.16 & 4.17 & 450.97 & 1.5 & 9.2 \\
7 AG & 29.59 & 4.57 & 494.81 & 1.7 & 9.2 \\
8 AG & 31.11 & 4.12 & 445.67 & 1.5 & 9.2 \\
9 AG & 30.39 & 3.37 & 364.29 & 1.2 & 9.2 \\
10 AG & 30.62 & 3.48 & 376.84 & 1.6 & 9.2 \\ Average Value & 30.08 & 3.83 & 414.74 & 1.54 & 9.2 \\ Standard Deviation & 0.99 & 0.63 & 68.67 & 0.31 & - \\ COV (\%) & 3.30 & 16.56 & 16.56 & 20.35 & - \\ \hline \end{tabular}
\end{table} 
Table 4: Tensile tests results for aged specimens in alkaline bath. Figure 8: Typical stress-elongation curves for GFRP tested specimens. program, even if a reduction of mechanical properties was expected, it is felt that the tested specimens have shown a high retention of the mechanical properties, which are strongly related to the design values that should be assumed in the long-term.
## 3 State-of-the-Art Review on Energy Optimization Techniques in UIoT
### Wireless Power Transfer Techniques for UIoT
#### 3.1.1 Underwater Acoustic Wireless Power Transfer (UA-WPT)  
The existing UA-WPT techniques are discussed in [158; 159; 160; 161]. For example, [PERSON] et al. [55] describe the core techniques of ultrasonic wireless power based on piezoelectric transducers. [PERSON] et al. [159; 24] developed an ultrasonic wireless power transfer approach for UIoT, and [PERSON] [161] proposed a novel underwater wireless high power transmission methodology based on an acoustic transducer array.
## 4 Summary of the Present Model  
The governing equations with complete closure models that were developed in the previous sections are presented here.  
Momentum equation:  
\[\rho\frac{\mathrm{D}U_{i}}{\mathrm{D}t}=-\frac{\partial\overline{p}}{\partial x _{i}}+\rho\frac{\partial}{\partial x_{k}}\left[(\
u_{s}+\
u_{T})\frac{ \partial U_{i}}{\partial x_{k}}\right]+\rho\frac{\partial}{\partial x_{k}} \left(\frac{\
u_{p}}{\lambda}\left[f(C_{nn})C_{ik}-\delta_{ik}\right]\right), \tag{34}\]  
where the eddy viscosity is given by  
\[\
u_{T}=C_{\mu}f_{\
u}\frac{k^{2}}{\varepsilon^{N}}, \tag{35}\]  
with modified damping function  
\[f_{\
u}=(1-A)\left[1-\exp\left(-\frac{y^{*}}{a_{\mu}\left(1+B/a_{\mu}\right)} \right)\right]^{2}, \tag{36}\]  
\[A=C_{A}\left(f_{N}\frac{\lambda^{2}\tilde{L}^{3/2}}{f(C_{kk})^{2}}\frac{ \varepsilon}{\
u_{0}}\right)^{0.3}, \tag{37}\]  
\[B=C_{B}(C_{kk}-3)^{1.25}/L, \tag{38}\]  
with constants \(a_{\mu}=26.5\), \(C_{A}=0.071\) and \(C_{B}=0.44\). \(y^{*}\) is given by Equation (31).  
Conformation tensor equation:  
\[\frac{\mathrm{D}C_{ij}}{\mathrm{D}t}-M_{ij}-NLT_{ij}=-\frac{1}{\lambda}[f(C_{ kk})C_{ij}-\delta_{ij}], \tag{39}\]  
with  
\[NLT_{ij}\approx f_{N}C_{N1}\frac{\lambda\sqrt{L}\varepsilon^{N}}{\
u_{0}f(C_ {mm})}\delta_{ij}-f_{N}^{1/4}C_{N2}M_{ij}+C_{N3}\frac{k}{\
u_{0}}\sqrt{\frac{ LM_{nn}}{\dot{\gamma}}\frac{\partial U_{i}}{\partial x_{k}}\frac{\partial U_{j}}{ \partial x_{k}}}{\dot{\gamma}^{2}}, \tag{40}\]  
where \(f_{N}=\
u_{T}/\
u_{0}\) is the local eddy viscosity, \(\dot{\gamma}=\sqrt{2S_{pq}S_{pq}}\) is the shear rate invariant, \(\tilde{L}=\sqrt{L^{2}/900}\) is the normalised maximum extension with the lowest DR, with model constants \(C_{N1}=0.11\), \(C_{N2}=0.3\) and \(C_{N3}=0.3\).  
Transport equation of \(k\):  
\[\rho\frac{\mathrm{D}k}{\mathrm{D}t}=\rho\frac{\partial}{\partial x_{i}}\left[ \left(\
u_{\mathrm{s}}+\frac{f_{\mathrm{t}}\
u_{\mathrm{T}}}{\sigma_{k}}\right) \frac{\partial k}{\partial x_{i}}\right]+P_{k}-\rho(\tilde{\epsilon}^{N}+D)- \frac{\
u_{p}}{\lambda}f(C_{mm})\frac{NLT_{mm}}{2}, \tag{41}\]  
where \(P_{k}=-\rho\overline{u_{i}u_{j}}\frac{\partial U_{i}}{\partial x_{j}}\) is the rate of production of \(k\).  
Dissipation transport equation:  
\[\rho\frac{\mathrm{D}\tilde{\epsilon}^{N}}{\mathrm{D}t}= \rho\frac{\partial}{\partial x_{i}}\left[\left(\
u_{\mathrm{s}}+ \frac{f_{\mathrm{t}}\
u_{\mathrm{T}}}{\sigma_{\mathrm{\varepsilon}}}\right) \frac{\partial\tilde{\epsilon}^{N}}{\partial x_{i}}\right]-f_{2}C_{\mathrm{ \varepsilon}2}\rho\frac{(\tilde{\epsilon}^{N})^{2}}{k}+\rho E\] \[+\left(C_{\mathrm{\varepsilon}_{1}}P_{k}-C_{N4}\
u_{p}\sqrt{C_ {\mathrm{\mu}}f_{\mathrm{\mu}}}\tilde{\epsilon}^{3/4}\left(\frac{k}{\
u_{0}} \right)^{2}\right)\frac{\tilde{\epsilon}^{N}}{k}, \tag{42}\]  
with model constant \(C_{N4}=0.083\).  
The remaining constants are from the Newtonian model and are \(C_{\mathrm{\varepsilon}_{1}}=1.45\), \(C_{\mathrm{\varepsilon}_{2}}=1.90\), \(C_{\mathrm{\mu}}=0.09\), \(\sigma_{k}=1.1\) and \(\sigma_{\mathrm{\varepsilon}}=1.3\).
## 3 Application
### Comminution  
As one of the most important processes for treating particles, comminution technology widely exists in engineering fields, such as pharmacy, food, agriculture and chemistry. Through the joint action between the high-speed rotating rotor and the static stator, the pulverizer can rapidly reduce the particle size under dry conditions, which is invariably accompanied by the action of fluid. In the whole process of particle collision or fragmentation, the fluid phase will guide the particle movement and provide kinetic energy. For the comminution process of particles, further analysis needs to be carried out in combination with the fluid phase.  
In the simulation of a comminution process by CFD-DPM, more attention is paid to particle tracking and particle-wall interaction. For instance, in the impact crusher [44; 45; 46], the particle and flow behavior after the collision of high-speed particles accelerated by air flow with stator and wall were studied, which explained the mechanism of the particle impacting the wall to a certain extent. However, there are two problems that must be solved: On the one hand, the input of a large number of particles will inevitably produce collision. Ignoring the collision between particles may have a great impact on the accuracy of the simulation results of the comminution process. On the other hand, because the particles in the discrete phase method are rigid bodies and breakage cannot be directly simulated, it is necessary to collect the data of particle collision and carry out post-processing to further simulate fragmentation.  
In dealing with particle collision and fragmentation, DEM using the soft-sphere model is more mature, which can simulate particle breakage and attrition by forming a particle by bonding a finite number of children particles or constructing a fragmentation function [47; 48]. Aiming at the problem that the DPM cannot deal with breakage, [PERSON] et al. proposed a new particle breakage model based on the simulation of particle motion by CFD-DPM [49]. If the impact stress is larger than the particle strength, the particle is broken and replaced with smaller fragments, and its effectiveness is verified (Figure 4). However, there is still a high amount of calculation.  
Although there are methods that can solve the problems of particle collision and fragmentation in the comminution process, such as building a breakage model for post-processing and DDPM, CFD-DPM is more suitable for particle tracking. The DEM method  
Figure 4: Particle distribution in grinding chamber (**a–d**) [49].  
has been further developed in terms of more microscopic interior or surface behavior. DPM and related methods have a lot of room for development.
## 4 Experimental Validation
### Damping System Effectiveness: Dynamic Behaviour of Active Boring Bar When Control Is On
#### 4.2.4 Experimental Results  
At first, the system was excited by single frequency force (pure sine) at 240 Hz (close to the resonance frequency of the system). Results in case of control on and off (Figure 20), show how the active damping system is able to significantly mitigate vibration (reduction of about 85%).  
Then, the system was tested by exciting the active boring bar with a multifrequency force (chirp sine between 0-500 Hz). Results in case of control off and on (Figure 21) highlight the effectiveness of the active system in adding damping to the system: vibrations in the frequency range close to the natural frequencies are significantly reduced. Outside, these frequency ranges in the control do not affect the vibrations level, as expected, and no distortions were found.  
Figure 19: (**a**) Active Boring bar. (**b**) Experimental setup.  
Figure 20: Measured displacement in case of control on and off in case of single frequency force applied.  
The same result was found in case of impulsive excitation, reproduced by impact testing with instrumented hammer (PCB 08C03) on the tool-tip and acquiring boring bar displacements by using the laser sensors (Figure 22).  
Frequency Response Functions highlight that active boring bar can reduce the amplitude of vibration at the natural frequency of more than 90%. In conclusion, preliminary experimental test on active boring bar confirmed the effectiveness of the active damping system, showing a drastic reduction vibration amplitude in correspondence of the dominant mode shape.
## 5 Ordered Pair Bat Algorithm for Optimizing the Scheduling Problem
### Analogy of the Batch Scheduling Problem  
The batch scheduling problem for hot rolling can be analogized to the traveling salesman problem (TSP). The TSP is a classic combinatorial optimization problem, which has applications in many engineering aspects [31]. Its mathematical description can be expressed as given the coordinates of n cities and the distance between each pair of cities,with the goal of minimizing the total distance, select a connection diagram that can cover all cities, where each city can only pass through once.  
Due to their similarity in modeling, the concept of TSP was borrowed to schedule batches in the practical engineering problem of continuous casting-hot rolling. When defining the sorting of batches, the batch numbers can be analogized as the city numbers in the TSP, the energy waste between batches can be analogized as the distance between cities, and the objective function of minimizing the total energy waste between continuous casting and hot rolling can be analogized as the minimum total distance in the TSP. Specific descriptions are shown in Table 2.  
In the above description, learning from the idea of the distance between adjacent cities in the TSP, an analogy was made to the energy waste in batch scheduling of hot rolling, with the aim of better scheduling the hot-rolling batches to calculate the energy waste. The specific solution can be found in Section 5.2. However, the comprehensive scheduling problem for continuous casting and hot rolling proposed in this study is not a complete TSP standard problem. In addition to batch scheduling, it also needs to consider the scheduling of continuous casting and the matching problem between continuous casting and hot rolling, which is a complex engineering problem.
## 2 Materials and Methods
### Mix Design  
Mix design is demonstrated in Table 3 and the quantity of each raw material is identified. 12 batches of specimens plus one reference sample with no additives were investigated and parameters encompassing the dosage of CNTs as well as NS were studied. 73% of FAC bcw was used as fine aggregate in the experiment. 56% w/c ratio was considered based on the relevant literature to ensure the dispersion quality of nano additives and the workability of cement mortar. According to [PERSON] and [PERSON] [44], in a system with a high w/c ratio, more water was provided for the better dispersion of CNTs. Further, owing to the hollow and spherical shape of FAC, air can be incorporated during the preparation process of the mortar, leading to a higher required amount of water to guarantee consistency and homogeneous flow [45]. The dosage of CNTs and NS is a prerequisite influencing the extent of improvement in the properties of cement materials. [PERSON] and [PERSON] [46] mentioned that the growing content of NS can lead to the improvement of the mechanical properties before the content reached the value of 4% bcw. [PERSON] et al. [47] and [PERSON] [48] proved that 1% of NS bcw accounted for the optimal mechanical performance of cementitious composites. [PERSON] et al. [49], [PERSON] et al. [50], and [PERSON] et al. [51] indicated that before the content of CNTs exceeded 0.5% bcw, along with the increase in the amount of CNTs, the mechanical behavior was positively enhanced. Based on the literature, the NS dosage of 0.20%, 0.60%, and 1.00%, as well as the CNT content of 0.05%, 0.15%, and 0.45% bcw were selected for the mix design. In Table 3, the abbreviation RS indicates the reference sample with only OPC, FAC, and water; C means the content of CNTs, and S illustrates that of NS. For instance, the index C5S2 indicates the samples containing 0.05% of CNTs and 0.2% of NS. The percentage of the raw materials is based on cement mass.
## 3 Results and Discussion
### CO\({}_{2}\) Emissions
For example, burning of 1000 g of fossil-based diesel in a stochiometric combustion releases 3158.1 g of CO\({}_{2}\) (with a molar mass of 44.01 g/mole), while burning the same mass of DSO biodiesel produces only 2703.6 g of CO\({}_{2}\) because the same mass of the larger-molecule DSO biodiesel contains a lower number of moles. As a consequence, burning DSO biodiesel releases less CO\({}_{2}\) than the same mass of fossil diesel. It is worth noting that the hydrogen content in DSO biodiesel is close to that in fossil diesel and increasing the H\({}_{2}\) fraction makes the combustion process more efficient. However, carbon also enhances the combustion process, and the DSO biodiesel have a lower carbon content than fossil diesel. Overall, higher C and H\({}_{2}\) fuel content enhances diesel engine efficiency. The findings of this study agree with other reports in the literature that CO\({}_{2}\) emissions decrease when biodiesel is combusted in a diesel engine [41; 42].
## 2 Chemical Composition of _Chlorella vulgaris_
### Carbohydrates and Fibres  
Carbohydrates constitute approximately 12-20% of _C. vulgaris_'s dry weight [4]. These carbohydrates primarily exist as polysaccharides, which have been recognized for their immune-stimulating and antioxidant properties [39]. The polysaccharides in _C. vulgaris_ are primarily composed of glucose, rhamnose, mannose, and galactose, and they include complex structures such as beta-glucans that contribute to the organism's biological activities and are particularly well-known for their significant immunomodulatory effects [40]. Additionally, _C. vulgaris_ contains small amounts of simple sugars, such as glucose, as well as glycogen, a polysaccharide that serves as an energy reserve [4].  
Furthermore, _C. vulgaris_ contains sulphated polysaccharides, which are known for their antiviral and immunomodulatory effects. These sulphated polysaccharides may enhance the immune response and inhibit viral replication by blocking viral adsorption and entry into host cells, further contributing to the health benefits associated with _C. vulgaris_ consumption [41; 42; 43].  
In addition to its carbohydrate content, _C. vulgaris_ is also a notable source of dietary fibers, which constitute a significant portion of its biomass [44]. These fibers, primarily composed of insoluble cellulose and hemicellulose, contribute to the structural integrity of the cell walls while also playing a beneficial role in human digestion and gut health [3]. The insoluble fibers in _C. vulgaris_ promote bowel regularity by increasing stool bulk and facilitating movement through the digestive tract. They also provide a substrate for beneficial gut microbiota [7]. Furthermore, _C. vulgaris_ contains soluble fibers such as pectins and beta-glucans, known for their ability to form viscous solutions that help regulate blood glucose levels and reduce cholesterol absorption [45].  
When incorporated into animal feed, these fibers can improve digestive efficiency, promote gut health, and enhance nutrient absorption in livestock. The fermentable fibers in _C. vulgaris_ provide a beneficial prebiotic effect, supporting the growth of beneficial gut bacteria, which may lead to improved immune function and overall health in animals. This combination of soluble and insoluble fibers not only enhances the nutritional profile of _C. vulgaris_ for human consumption but also makes it a valuable component in animal feed formulations, contributing to improved digestion and health outcomes in livestock.
## 1 Introduction  
Traffic bottlenecks significantly reduce the safety and efficiency of transportation systems [1, 2, 3]. At bottlenecks, flow is restricted due to accidents, reduced speed limits, and lane merging and closure [1, 4], which lowers road capacity. When the upstream traffic demand surpasses the bottleneck capacity, vehicles form a queue upstream, leading to congestion [5, 6]. Further, when a downstream vehicle approaches a bottleneck, the speed is reduced resulting in shockwaves. These waves propagate upstream and increase the risk of collisions [4]. Traffic congestion due to bottlenecks causes delays, which according to the United States urban mobility report has doubled from 1982 to 2019 [7]. To address this issue,safety management and control measures such as ramp metering, variable speed limit (VSL) control, and dynamic routing have been adopted [2; 4]. However, their effectiveness is limited by the VSL range and maximum queue length permitted on a ramp [1].  
The recent introduction of connected autonomous vehicles (CAVs) has led to the development of methods to improve traffic flow efficiency at bottlenecks [8]. These typically involve CAV trajectory planning and merging control, and have been shown to provide effective congestion control [9; 10]. CAVs can enhance VSL control performance and create an environment for vehicle-to-vehicle (V2V) and/or vehicle-to-everything (V2X) data exchange via wireless communication [4]. This enables vehicles to obtain downstream traffic information beyond visual perception. Connected vehicles can increase road capacity and decrease emissions by reducing time headway and improving reaction time and sensitivity to changing traffic conditions [11].  
The CAV market penetration rate has increased rapidly, and it is anticipated that 75% of vehicles will be CAVs by 2050 [12; 13]. In Saudi Arabia, this market is estimated to increase 15.3% by 2030 [14]. The Kingdom aims to transform its traffic system through connected vehicle technology [15]. CAVs offer environmental, societal, and economic benefits that align with Saudi Vision 2030. The development of future cities such as Ha'il, Neom, and The Line is increasing CAV demand. However, the connected vehicle environment is susceptible to cybersecurity threats. Therefore, the behavior of CAVs at bottlenecks considering cyberattacks needs to be investigated.  
The contributions of this paper are as follows:  
* A microscopic model based on traffic bottleneck data from the roadside is developed. This is crucial to evaluate and characterize CAV behavior.
* The proposed model is evaluated and compared with state-of-the-art models. The results obtained indicate that CAV behavior at bottlenecks is accurately and realistically characterized.
* CAV behavior at bottlenecks under cyberattacks is investigated to help address cyber-security challenges.
* The proposed model is assessed for worst-case traffic conditions on a circular road as vehicles cannot leave. It is shown that CAV behavior is stable as the variations decrease over time.