esa-sceva/satcom-chunk-collection huggingface dataset:
# Experimental Evaluation of Collision Avoidance Techniques for Collaborative Robots
## 5 Discussion  
In all the avoidance modes, the differences between simulated and real data resulted less than 0.02 rad for joint positions and 0.1 rad/s for joint speeds. Thus, it can be concluded that the theoretical algorithms revealed to be effectively applied to a real robotic system showing behavior similar to what simulations expect. Only a few parameters, i.e., proportional gains, need a trial and error setting procedure to optimize the response of the system.  
Diagrams of the three modes in which the robot can execute a trajectory to avoid the obstacle were proposed for each test case. In this way, it is possible to analyze the behavior of the system in the case when some degrees of freedom related to orientation cannot be updated during motion. Even though only the three translation-related DOFs are exploited in Mode III to replan the motion, the system still shows good obstacle avoidance capability, and the variation in position and velocity between the real and simulated data remains negligible: the maximum position error in mode III is about 0.13 rad, whereas the same value for mode I is 0.11 rad. In general, the difference between simulated and real data may depend on many variables. The most important ones are  
1. the communication protocol between the external controller and the robot, thus, the frequency of data exchange;
2. the accuracy of the robot controller in driving the motors via internal control loops to follow the external speed reference signal; this characteristic may vary among different manufacturers or robot models;
3. the acquisition frequency of external sensors for obstacle detection, which should be as high as possible or at least comparable to the control frequency;
4. the complexity of the algorithm, which, if too high, can increase the computation time, thus, reducing the speed of the control loop.  
In this work, the use of a UR5e robot allowed easy integration with the external controller at a frequency of about 350 Hz, a remarkable achievement when compared with previous work (e.g., 250 Hz in [27]). However, so far, the position of the obstacle has been generated virtually to eliminate any sensor error. Future integration of a real sensor system could lower the control loop frequency, which should still remain high enough to ensure smooth control of the robot, as experienced in [28] where a final control rate of about 70 Hz revealed sufficient to have an efficient real-time control. In summary, the results show that the algorithm is indeed implementable on the real system and processable even by a standard PC, thus, having a low economic impact.
# TETES: Trust Based Efficient Task Execution Scheme for Fog Enabled Smart Cities
## 6 Conclusions  
Malicious nodes disturb the quality of service of IoT-based wireless networks in smart city applications. Limited IoT nodes' processing capacity requires offloading of tasks to high computing nodes such as fog nodes. Malicious nodes present in the network try to disturb the performance of fog nodes by offloading complex natural tasks that require more computing power and resources. This compromises the execution performance of the fog node causing delay in task execution of legitimate nodes. TETES is a trust-based efficient task execution scheme for fog nodes, that discards the offloaded tasks executed by fog nodes and efficiently executes the tasks offloaded by legitimate nodes. The performance of TETES is compared with well-known STF, LTF, and FCFS algorithms in calculating number of tasks offloaded by trusted nodes, the executed data of offloaded tasks and the mean trust values for varying processing capacity of fog nodes and varying number of offloaded tasks. The results show that TETES executes 33%, 125%, and 60% more tasks as compared to STF, LTF, and FCFS respectively for varying processing capacity of fog nodes and 40%, 189%, and 93% tasks for increasing number of offloaded tasks as compared to STF, LTF, and FCFS respectively. It is evident from the results that the mean trust value of executed tasks in TETES is higher than any other scheme in all varying results. In future, we will develop a distributed and cooperative trust management scheme for task offloading in IoT networks.  
Conceptualization, A.N.A., B.A. ([PERSON]), M.S.S., M.A., D.A. and B.A. ([PERSON]); Writing--original draft, A.N.A., B.A. ([PERSON]), M.S.S. and M.A.; Writing--review & editing, D.A. and B.A. ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
This work was supported and funded by the Deanship of Scientific Research at Imam Mohammad Ibn Saud Islamic University (IMSIU) (grant number IMSIU- RG23112).  
Figure 11: Mean Trust Value of Executed Tasks for Varying Number of Requested Tasks.
## 3 OTFS Channel Estimation
### Pilot Model  
In our scenario [8; 11], the single-antenna transmitter sends a pilot symbol in the DD domain. The corresponding pilot frame is given by  
\[X[m,n]=\begin{cases}\sqrt{E_{p}}&m=m_{p},\;n=n_{p},\\ 0&\text{otherwise},\end{cases} \tag{8}\]  
where \(m=m_{p}\), \(n=n_{p}\) is the delay-Doppler resource element (DDRE), in which the pilot is sent and \(E_{p}\) is the energy of the transmitted pilot signal.  
At the receiver, after the operations described above and vectorization, the received pilot frame is given by  
\[\mathbf{y}=\text{vec}(\mathbf{Y})=\mathbf{H}\text{vec}(\mathbf{X})+\mathbf{w}= \mathbf{H}\mathbf{x}+\mathbf{w}, \tag{9}\]  
where \(\mathbf{w}\sim\mathcal{CN}(0,N_{0}\mathbf{I})\in\mathbb{C}^{MN\times 1}\) is the AWGN noise vector and \(\mathbf{H}\in\mathbb{C}^{MN\times MN}\) is the delay-Doppler domain channel matrix, given by  
\[\mathbf{H}=\sum_{i=1}^{P}g_{i}\mathbf{G}_{i}(\tau_{i},\
u_{i}), \tag{10}\]  
where \(\mathbf{G}_{i}(\tau_{i},\
u_{i})\in\mathbb{C}^{MN\times MN}\) is a matrix that captures the effect of the propagation delay and the Doppler shift of the \(i\)-th path, and for \(l^{\prime},l^{\prime\prime}=0,1,..,M-1\), \(k^{\prime},k^{\prime\prime}=0,1,...,N-1\) is computed as [8; 11]  
\[G_{i}[k^{\prime}M+l^{\prime},k^{\prime\prime}M+l^{\prime\prime}]=\frac{e^{-j /2\pi\tau_{i}\
u_{i}}}{MN}\sum_{n=0}^{N-1}\sum_{m=0}^{M-1}r_{k^{\prime\prime}, l^{\prime}}(m)e^{j\pi\left(\frac{\pi}{M}\left(l^{\prime}-l^{\prime\prime}-\frac{M \tau_{i}}{T}\right)-\frac{\pi}{N}\left(k^{\prime}-k^{\prime\prime}-\frac{N\
u _{i}}{M}\right)\right)}, \tag{11}\]  
where  
\[r_{k^{\prime\prime},l^{\prime}}(m)=\sum_{s=-m}^{M-1-m}e^{j2\pi s \frac{l^{\prime}}{M}}\Bigg{[}\left(1-\frac{\tau_{i}}{T}\right)e^{j\pi\left(1+ \frac{\tau_{i}}{T}\right)\left(\frac{\tau_{i}}{M}-s\right)}\text{sinc}\Big{(} \left(1-\frac{\tau_{i}}{T}\right)\Big{(}\frac{\
u_{i}}{\Delta f}-s\Big{)} \Big{)}+ \tag{12}\] \[\frac{\tau_{i}}{T}e^{-j2\pi\frac{l^{\prime\prime}}{N}}e^{j\pi \left(\frac{\tau_{i}}{T}\right)\left(\frac{\tau_{i}}{\Delta f}-s\right)}\text{ sinc}\Big{(}\tau_{i}\big{(}v_{i}-s\Delta f\big{)}\Big{)}\Bigg{]}.\]  
If the channel coefficients are normalized such that \(\sum_{i=1}^{P}|g_{i}|^{2}=1\), the received pilot signal power equals the transmitted one. Under this hypothesis, the pilot signal-to-noise ratio (PSNR) can be computed as in [8]. Hence, the pilot signal power is the total energy \(E_{p}\) divided by the overall time duration \(NT\), while the noise power is the product of the power spectral density \(N_{0}\) and the bandwidth \(M\Delta f\). Thus,  
\[\text{PSNR}=\frac{E_{p}/NT}{M\Delta fN_{0}}=\frac{E_{p}}{MNN_{0}}. \tag{13}\]  
During a channel coherence window, the receiver processes the received pilot frame running a channel estimation algorithm, and once the estimated channel matrix \(\hat{\mathbf{H}}\) is obtained, uses the estimate to detect the incoming data frames.
# A Practical Positioning Method in End-Plate Surface Distance Measurement with Nano-Meter Precision
## 2 System Description  
The system block diagram is shown in Figure 1. The system is mainly composed of two parts that realize positioning and measuring function. The positioning part is the key of the system. The measured 0.1 mm step is made first, as shown in Figure 1, where A, B, and C are the standard gauge blocks. The optical path of positioning interference is the simple Michelson type. The laser beam and white light beam are polarized first by polarizer and then separated by PBS and NPBS (PBS is polarizing beam splitter, NPBS is non-polarizing beam splitter) to transmit to each receiver. Three photoelectric receivers are used for receiving three interference signals, i.e., the positioning signals of white light interference and laser interference as well as laser interference signal. The positioning laser and displacement measurement laser use the same laser source of semiconductor laser. The positioning laser and displacement measurement laser are separated by a splitter and transmitted into each optical path. The light beam collimating and expanding unit is used for reducing the error caused by the diffraction of the Gaussian laser beam. The signal processing system can simultaneously sample and record the position interference signal and laser interference displacement signal, and then process these signals to get the value of the height of the step. There are two design features. The first feature is the position optical path, which is designed to be suitable for the cooperation of laser and white light interference, and the other feature is the signal processing system designed for dynamic measurement.  
In fact, the noise and error are inevitable, and actual white interference signal is shown in Figure 4; the noises and shape defection is obvious by comparison with the theoretical signal shown in Figure 5. The noise and shape defection are the main factors that reduce the positioning repeatability of measurement. How to reduce the error caused by the noise and the defection is to be studied. The basic measure is to enhance the signal to noise ratio of the interference signal by optimizing the system and filtering the signal. For this purpose, the surface is positioned by the combination of laser interference and white light interference, the rough position is obtained by the white light interference signal, and the accurate position is obtained from the laser interference positioning signal.  
Figure 4: Actual white light positioning signal for surface.  
Figure 3: Positioning signal of 0.1 mm step surface.  
Figure 2: The interference pattern of white light and RGB light interference components. (**a**) White light interference pattern; (**b**) red light interference pattern; (**c**) green light interference pattern; (**d**) blue light interference pattern.
###### Abstract  
In this study, a model was proposed to predict the corrosion rate (Mils per Year, MPY) of carbon steel in a 3.5% NaCl solution, with the objective of comparing the effectiveness of a commercial LPR sensor against traditional electrochemical methods, using potentiostat-based LPR techniques. The primary factors considered in the experiments were temperature, flow velocity, and pH, tested through a full factorial design to identify the most influential variables. Statistical analysis showed that temperature and flow velocity had a significant effect on corrosion rate, with their interaction having the most substantial impact. In contrast, pH had no statistically significant influence within the tested conditions, likely due to the dominant effects of temperature and flow velocity in the high-salinity environment. The MPY data were validated through Tafel plots, immersion coupon tests, and other electrochemical techniques to confirm the reliability of the measurements. A regression model trained on 54 MPY data points demonstrated high accuracy, achieving a coefficient of determination (R\({}^{2}\)) of 0.9733. The model also provided reliable predictions for factor combinations excluded from the training dataset. Additionally, scenario-based evaluations highlighted the model's performance under simulated operating conditions, while revealing challenges related to sensor contamination during long-term use. These findings emphasize the potential of commercial LPR sensors as effective tools for real-time corrosion monitoring and demonstrate the utility of the regression model in marine environments.  
carbon steel; corrosion rate; LPR sensor; electrochemical technique; regression model +
Footnote †: journal: Applied Sciences  
1
# Evaluation of _Aspergillus flavus_ Growth on Weathered HDPE Plastics Contaminated with Diesel Fuel
###### Abstract  
Plastic containers used for diesel storage represent an underexplored but significant environmental challenge due to hydrocarbon retention and prolonged weathering. This study evaluates the capacity of _Aspergillus flavus_ to colonize and grow on high-density polyethylene (HDPE) surfaces contaminated with weathered and fresh diesel residues. Circular plastic samples from HDPE tanks exposed to environmental conditions for over two years (weathered) and for less than two months (non-weathered) were inoculated with _A. flavus_ and incubated at 20 \({}^{\circ}\)C, 25 \({}^{\circ}\)C, and 30 \({}^{\circ}\)C. Growth kinetics were assessed through radial expansion and halo formation, quantified via digital imaging and ImageJ analysis. Results showed the most robust fungal growth occurred on weathered diesel-contaminated gray plastics at 30 \({}^{\circ}\)C, with colony areas exceeding 350 mm\({}^{2}\) and halos over 3000 mm\({}^{2}\). Conversely, white HDPE with fresh diesel showed limited and inconsistent growth, likely due to the presence of volatile hydrocarbons and polymer additives. These findings underscore the critical role of diesel aging and polymer characteristics in shaping fungal adaptability, providing a foundation for the development of environmentally sustainable bioremediation strategies targeting diesel-contaminated HDPE plastics.  
A _Apergillus flavus_; bioremediation; diesel contamination; HDPE; fungal colonization; weathering; halo formation; temperature effect
## 1 Introduction
### Motivation and Contributions  
In order to simultaneously improve the communication performance, transmission distance, and coverage range of the communication system, this paper analyzes the performance of a UAV-assisted multi-hop parallel hybrid FSO/RF communication system (abbreviated as UAV-assisted hybrid system) that combines a hybrid FSO/RF parallel transmission system and a parallel multi-hop relay cooperative communication system by the UAV relay. Our main contributions in this work are pointed out as follows:  
* To our knowledge, we, firstly, propose a multi-hop parallel hybrid FSO/RF communication system architecture with and without PE based on the UAV relay.
* New mathematical expressions for the end-to-end system in terms of average bit error rate and outage probability are derived under EW turbulence and Nakagami fading channels for four binary subcarrier modulation schemes.
* The effects of different weather environments, modulation methods, receiver apertures, RF fading parameters, pointing errors, and relay structures on the performance of our considered systems are analyzed through numerical evaluationnumerical simulations. As far as we know, no existing work considered the impact of weather environments and aperture averaging on UAV-assisted FSO communication.  
The rest of the paper is structured as follows: in Section 2, the system model is presented. Section 3 analyzes the average bit error rate and outage probability. The performance of the hybrid FSO/RF direct link and UAV-assisted hybrid FSO/RF communication system is analyzed by system simulation in Section 4. Finally, conclusions are given in Section 5.
# Simulation Tool for Winter Navigation Decision Support in the Baltic Sea
## 8 Verification and Validation of the Simulation Model
### Validation
#### 8.2.2 Face Validity  
The role of experts in model verification has been detailed in Section 8.1. Apart from verifying adherence to conceptual descriptions, the experts also carefully observed the waiting time averages at different ports and the average waiting times for all the vessels. Some of these experts were involved during actual field assistances for the same time period. They validated that the obtained KPIs were within acceptable ranges for that winter. Furthermore, the number of assistances for each icebreaker were noted and validated.
## 3 Methodology
### Second Phase: Comparative Survey Method  
After using the Delphi methodology, and to complement it, a survey was launched to compare the savings of drivers with connected vehicles with those with non-connected vehicles regarding car mobility scenarios (i.e., the vehicle is in a certain area at a certain time, so it is advisable to suggest a certain action to the driver) and the vehicle itself (e.g., it needs fuel, servicing, or an oil change). This method was used to confirm the findings of the experts in the exploratory phase. The idea of the survey was to confirm if the determinants driven by the experts are indeed those with the highest impact on real scenarios or not. This survey was part of a global data collection process performed within this research. The whole questionnaire can be consulted in the following link in the author's mother language ([[https://drive.google.com/file/d/13](https://drive.google.com/file/d/13) EliXG18Z6F-xexgUclirUOxOr-9 iMZB/view]([https://drive.google.com/file/d/13](https://drive.google.com/file/d/13) EliXG18Z6F-xexgUclirUOxOr-9 iMZB/view), accessed on 18 January 2022), or in Appendix A. Hence, a survey was launched in an online format, again using Google Forms, in order to evaluate and compare the spending of drivers of connected and non-connected vehicles. In both cases, the link to the online form was sent by email. Such surveys were sent out to 1500 drivers with connected vehicles, and a snowball sampling technique [35; 36] was used to send the same surveys to drivers of non-connected vehicles. As will be shown later, 302 valid responses from users with connected cars and 254 responses from drivers without connected cars were collected. Both sample datasets (for connected and non-connected vehicles) have the same country and the same geographical area; there was no electric vehicle in the sample. This sample is significant since it represents the average age of cars in Spain and includes people with very similar social-demographic profiles in order to avoid any kind of statistical bias. This made it possible to compare the groups and determine the impact of the stimuli received by drivers of connected cars on their expenditures and savings. The authors of this text reached an agreement with the Grupo Next company (www.gruponext.es and www.nextsmartcar.es, accessed on 18 December 2021) to provide them with a sample of 1500 drivers of connected vehicles. This company is one of the European actors in the field of connected cars and offers users different services to make car-related activities less expensive and easier to manage. The completely anonymized sample of drivers making journeys in Spain was extracted from the company's database. In the case of the non-connected cars' drivers, through a snowball technique, we sent emails to different groups of collaborators who subsequently sent the emails to others, and these then sent them to many others
###### Abstract  
We propose a facile method to produce micro/nano hierarchical surface-enhanced Raman scattering (SERS) active substrates using simple steps and inexpensive costs. The proposed SERS substrate is a silicon pyramid array covered by a nanostructured gold film (AuNS @ SiPA). Through finite element method (FEM) simulation, we showed that many strong local electric field enhancements (hot spots) were formed between the nano-gap of gold nanostructures. In addition, the micron-scale pyramid structure not only increases the sensing surface area of the sensor, but also helps trap light. By combining these micro and nano structures, the proposed micro/nano hierarchical SERS sensor exhibited high sensitivity. Experimental results confirmed that the AuNS @ SiPA substrate has high sensitivity. The SERS signal enhancement factor obtained from the Rhodamine 6G (R6G) probe molecules was as high as \(1\times 10^{7}\) and the SERS substrates were found to be able to detect a very low concentration of 0.01 nM malachite green (MG) solution. Therefore, this study provides a novel and practical method for fabricating SERS substrates that can facilitate the use of SERS in medicine, food safety, and biotechnology.  
SERS; surface-enhanced Raman scattering; gold nanostructure; micro/nano hierarchical; micro-pyramid array; malachite green; MG Article
# An Improved CZT Algorithm for High-Precision Frequency Estimation
## 3 Improved CZT Algorithm
### Approximate Solution of the Bias Correction Factor \(\mu\)
Output:\(\hat{f}_{c}\)
1: N-point FFT algorithm is applied to \(x(n)\) to obtain its spectrum \(X(k)(k=0,\,1,\,\cdots,\,N-1)\); Find its index \(k_{p}\) corresponding to the maximum of \(X(k)\) and obtain the frequency estimate \(\hat{f}_{p}\);
2: Apply the conventional CZT algorithm to \(x(n)\) in the refined frequency range \((f_{1},f_{2})\);
3: Find the frequency index \(k_{m}\) corresponding to the maximum amplitude of the CZT spectrum;
4: Calculate the approximation of \(\mu\) using (24);
5: Calculate the approximation of \(\delta\) using (25);
6: Obtain the frequency estimate \(\hat{f}_{c}\) using (26). ```  
**Algorithm 1** Improved CZT algorithm.
## 2 Materials and Methods  
### Materials  
(1) Bentonite  
The test bentonite was produced in Anji County, Zhejiang Province, with the appearance of off-white powder. An X-ray diffractometer (XRD) test analysis showed that the bentonite was sodium-based bentonite, whose main components consisted of montmorillonite (78.50 wt.%), quartz (78.50 wt.%), and feldspar (5.60 wt.%), as shown in Figure 1. It had a relative density of 2.72, a liquid limit of 218%, a plastic limit of 43%, a plasticity index of 175, a total cation-exchange capacity of 0.77 meq/g, and a swelling capacity of 8.50 mL/g in deionized water (DIW).  
(2) Zeolite  
The test zeolite was produced from a water purification plant in Henan Province and was a grayish-white silicate mineral with a microporous structure and solid adsorption and ion exchange capacity. The silica-oxygen tetrahedron is the most fundamental building block of the zeolite framework. From XRD analyses, it has been shown that the primary zeolite constituents are SiO\({}_{2}\) (63.90%), Al\({}_{2}\)O\({}_{3}\) (13.60%), CaO (2.80%), MgO (2.40%), Fe\({}_{2}\)O\({}_{3}\) (1.30%), and K\({}_{2}\)O (2.80%). Loss-on-ignition for the zeolite was 12.83%.  
(3) Stone chips  
As shown in Figure 2, the stone chips, which formed the matrix of the soil mixture, were obtained from a quarry in Anji County, Zhejiang Province, with a maximum particle size of 5 mm, an acceptable particle content of 7.48%, a uniformity coefficient Cu of 11.78, and a curvature coefficient Co of 1.06. The resulting soil mixture had a satisfactory aggregate grade. According to the geotechnical experimental specification standard, we used the compaction test to obtain the optimum moisture content of the specimen of 7.50% and the maximum dry density of 1.99 g/cm\({}^{3}\). Through the sieving experiment, we obtained the gradation parameters of stone chips with d\({}_{50}\) = 1.10 mm, C\({}_{\rm u}\) = 13.27, and C\({}_{\rm c}\) = 1.82, which indicated that the experimental stone chips were well-graded. The variable head infiltration test showed that the average permeability coefficient of the stone chips was 1.76 \(\times\) 10\({}^{-4}\) cm/s at 100% compaction. The stone chips did not meet the permeability requirements of the landfill liner system, i.e., 1 \(\times\) 10\({}^{-7}\) cm/s.