esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Design
### Principles of Optical Detection  
The scattering cross-section can be (with the right combination of materials, size and wavelength) a sensitive function of particle size. Thus, when the particle vibrates, there will be a change in the scattering cross-section and the intensity of the scattered light will be modulated by the vibration, which constitutes the principle of detection in our work. Other factors, like the change in temperature of the particle/surrounding media/substrate, the photo-elastic effect and the very short-lived electronic contribution from the hot electrons, can affect the particle scattering, but we have neglected them. The temperature change results in a slowly varying thermal background in our signals, which is removed by signal processing, and the photo-elastic effect is thought to only provide a small contribution to the signal received (as this effect in gold is small [26; 27]) and the short-lived electronic effects are tied up in a \"co-incidence peak\", which is removed alongside the thermal decay. In this paper, we define the optical sensitivity as the change in scattered light over a 1 nm change in the radius of the particle. This shows the change in reflected/transmitted light during the vibration of the particle.  
Figure 2 shows the theoretical optical response as a function of the probe laser wavelength for gold nanoparticles of different sizes immersed in water, as given by the Mie model. The area studied in this work corresponds to a region where the probe laser wavelength is in the near infrared (>700 nm) and the particle size between 150-250 nm. Figure 2a is the radar backscattering cross-section, where a 220 nm nanoparticle exhibits the strongest scattering. Figure 2b is the optical sensitivity where 160 and 220 nm nanoparticles show high and low values over the wavelength range, respectively. Figure 2c is the  
modulation depth, defined as the optical sensitivity divided by the radar backscattering cross-section, which shows a region with high response for a nanoparticle smaller than 100 nm. This region is very high as the DC is nearly 0. Therefore, although this appears to be the best place to operate, the extremely low light return means that the signal-to-noise ratio (SNR) will be poor. Figure 2d shows the sensitivity with respect to SNR and here a particle with size \(\sim\)150 nm is optimal.  
Figure 2e shows the modulation depth for the three sizes of gold nanoparticles measured in this paper (150, 200 and 250 nm) as a function of the probe laser wavelength (the range of our setup) in air (lines) and water (dots) surrounding media. The chosen media for the study of the optical response of plasmonic nanoparticles is water where the optical response follows different tendencies for the three sizes in the available probe wavelength range. It is clear from there that the size of the particle, the probe laser wavelength and the surrounding media have a significant impact on the optical response. For example, the optical response of a 250 nm gold nanoparticle will increase a factor of \(\sim\)6 when the probe wavelength is tuned from 850 to 700 nm.
# Bifunctional Heterogeneous Catalyst for Biodiesel Production from Waste Vegetable Oil
## 4 Conclusions  
Numerous categories of heterogeneous catalysts have been investigated for transesterification. Nevertheless, there is still a need for meticulous studies to be conducted on feedstock containing high amounts of FFA. In this study, the use of bifunctional catalyst in the transesterification of waste vegetable oil was successful, notwithstanding the presence of free fatty acids. The optimum reaction conditions of temperature (65 \({}^{\circ}\)C), oil-to-methanol molar ratio (1:9), catalyst/oil loading (4 wt %), and reaction time (4 h) employed resulted in high FAME yield (89% for WPO and 98% for WSO). The results showed that 60% CaO/40% alumina by weight catalyst, calcined at high temperature of 750 \({}^{\circ}\)C for activation into metal oxides and transesterification reaction, effectively mediated the transesterification of WPO ([FFA] = 76.96 mg/g). However, due to the lower FFA content in WSO (0.3825 mg/g), a lower ratio of the acidic oxide was required, 80% CaO/20% alumina by weight catalyst. As shown in this study, an active bifunctional catalyst with high CaO dispersion could be prepared via the co-precipitation method as confirmed by the SEM analysis. However, the basic-acidic oxide ratio to be employed is dependent on the type of WVO and its FFAs content. The FAs content of WSO was 29.18% compared to 36.2% for WPO, which gave WSO better potential as a feedstock for FAME production. The esterification/transesterification reactions of WPO and WSO with CaO/Al\({}_{2}\)O\({}_{3}\) bifunctional catalyst resulted in the high yield of FAMEs: 89% for WPO and 98% for WSO at catalyst ratios (wt %) of 60% and 80% respectively. The WVO have great potential as an alternative source of marketable FAME, which is an environmentally friendly and cheap energy to replace fossil fuel.  
Conceptualization and validation, A.M.R., S.E., O.O.; methodology and software, A.M.R. and S.E.; Formal analysis[PERSON] O.O., S.E. and A.M.R.; Writing--Original draft preparation, S.E., A.M.R. and O.O.; writing--review and editing, A.M.R., O.O., B.O. and V.O.; supervision, A.M.R and O.O.; project administration, A.M.R. and O.O.; Visualization, A.M.R., O.O., V.O. and B.I.O. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the South Africa Technology Innovation Agency (TIA).  
We are grateful to the technical staffs of the Department of Chemical Engineering Cape Peninsula University of Technology.  
The authors declare no conflict of interest.
## 3 Results  
The TDS-1 data collected over the Arctic and Antarctic regions with the latitude above 55\({}^{\circ}\)N and 55\({}^{\circ}\)S from January 2015 to December 2018 are analyzed in this study. Twenty percent of the data is randomly selected to train the ML-based models to distinguish sea ice from water. The remaining 80% of data is used as the test dataset to validate the sea ice monitoring methods developed using ML algorithms.  
As aforementioned, the GNSS-R receiver on the TDS-1 satellite was not always in operation. Thus, the TDS-1 data are not accessible every day. Figure 5 presents the situation of data availability from January 2015 to December 2018. The data unavailability from August to October 2017 is probably due to the scheduled shutdown of TDS-1 mission, which was originally set to the end of July 2017. In fact, the TDS-1 mission was extended from February to December 2018. During its extension, the SGR-ReSl was operated every day, rather than the two of eight-day cycle as in the first three years. The coverage and sampling were increased by a factor of four. The data missing for a few days in 2018 may result from statutory holidays, such as Christmas.
# A Novel Data Forwarding Strategy for a Drone Delay Tolerant Network with Range Extension
## 2 Related Work  
The UAV flight path planning problems can be categorized [18] as (i) off-line planning; where the global information about the waypoints is available in advanced [19; 20; 21; 22], (ii) on-line planning; where the required information is partially known or completely unknown in advance [20; 21; 23], and (iii) cooperative planning; where a mission is too complex to be carried out by a single UAV and hence, a group of UAVs is involved in planning [19; 21; 24]. All these three types of problems have been proved to be NP-Complete [25]. We focus on utilizing the cooperative planning problem as we consider the impact of inter-connection between UAVs on path planning. Moreover, the global information regarding the waypoints is completely available and known to every single UAV, meaning that our approach also considers an off-line planning, which is the most commonly adopted approach to UAV path planning algorithms. Hence, we take advantage of cooperative planning because UAVs have limited energy budget that allows them to fly for a short distance. In addition, as data packets are collected, UAVs need to re-plan their paths. However, this path planning may not be possible as UAVs cannot add all the waypoints (packets' destinations) to their flight path due to the limited energy. Also, UAVs have to return to their origin base to pick up another assignment for delivery. The authors in [16] and [26] consider the closest waypoint strategy to identify at each step the next waypoint whether the UAV has enough power to return to the origin base. This implies that although the greedy search algorithm simply finds the next way point and establish the flight path, it impacts the performance of the data network. This is because packets have a limited time to live and if a UAV arrives at a waypoint when the data packet expired, the delivery ratio of the network decreases.
# Cheating Detection in Online Exams Using Deep Learning and Machine Learning
## 2 Materials and Methods
### Logistic Regression (LR)  
LR is a statistical model that classifies target variables by using them together with more independent variables. It measures the relationship between two or more variables to obtain descriptive inferential statistics [36]. At the same time, logistic regression is a method used to determine the cause-and-effect relationship with independent variables when the target variable has two or more categories [37]. Logistic regression is a generalization of linear regression [38]. Instead of modeling the output value directly in logistic regression, the probability of belonging to a class is modeled. Using the logistic function, the probability of belonging to a class has a value between 0 and 1 according to the attributes [39]. LR is a linear model. This model was preferred because it showed successful performance in classifying data belonging to two or more classes.
# Analysis of LTE-M Adjacent Channel Interference in Rail Transit
## 3 Interference Analysis of LTE-M Underground System
### Interference Analysis When LTE-M Uses Leaky Cable Radiations Pattern

Table 6: Interference calculation parameters of LTE-M with leaky cable radiations pattern. Figure 8: Influencing factors when UE interfering with BS: (**a**) transmitting power of interference signal, (**b**) L3.
# Challenges and Solution Directions of Microservice Architectures: A Systematic Literature Review
## 5 Identified Challenges and Solution Directions
### RQ2. What Are the Identified Solution Directions?
#### 5.2.4 Performance Prediction, Measurement, and Optimization
Study [CF] indicated that the number of studies addressing performance problems of MSA-based systems is limited. In this study, the authors propose a model-driven approach for continuous performance enhancements by defining some dedicated metamodels. This study provides refactoring actions that enable performance improvements by taking advantage of the relationship between the monitored data and the architectural model. This approach has been used on two MSA-based systems to validate its feasibility. In summary, the challenges of this topic are related to performance prediction, measurement, and optimization. With the wide use of microservice applications, it has become possible to create a wider solution set for performance problems. There are studies that address performance issues directly, as well as studies that address other problems such as scalability and load balancing and provide benefits at the point of performance. These studies have been evaluated in their own categories.
# Collision Risk Assessment and Operation Assistant Strategy for Teleoperation System
## 1 Introduction
Finally, the accuracy and effectiveness of the proposed collision risk model and assistant strategy were verified by simulation and experiments. In brief, this study provides an effective method for collision risk assessment and the obstacle avoidance of teleoperation robots. Apart from Section 1, the paper is organized as follows: Section 2 describes the system overview. The detail of graphical modeling is described in Section 3. Section 4 presents the collision risk assessment model. Section 5 introduces the assistant strategy of teleoperation. Next, in Section 6, the designed collision risk assessment model and assistant strategy are verified and analyzed by experiments. Finally, Section 7 summarizes the research of this paper and puts forward future work.
## 3 Results and Discussion
### Main Complexes Formed with Microcystins with High Affinity Scores
#### 3.1.1 Metabolic Effects and Insulin Regulation

Table 1: Main protein complexes formed with the four microcystin variants (MC-LA, MC-LR, MC-RR, and MC-LY) with binding energies lower than \(-10\) kcal/mol. In the case of MC-LA and MC-LR, they showed a strong interrelationship with receptors such as prolactin (PRL-R), MAPK14, and ATP-citrate synthase. These receptors positively regulate cellular proliferation, are activators of mitogenic protein phosphorylation, and participate in lipid synthesis, and their alteration is related to cancer induction [27,28]. The affinities found in MCs indicate that exposure to minority variants such as MC-LA and MC-LY could be high risk as they have a higher affinity for proliferation-inducing factors that alter important processes. It has been shown that these congeners may be dominant during cyanobacteria blooms, and therefore, the risks associated with water consumption by humans and other organisms are increased [17,29]. We found that, among the 189 interactions with insulin receptors, five of the interactions were specific to MC-LA and MC-LY with high affinity, presenting a higher binding energy with two important proteins (i.e., MLL1 and AKT3). MLL1 activates the methylation of histone H3K4 and therefore is involved in the proper development of embryogenesis, differentiation in hematopoiesis, and neurogenesis [30]. In addition, MC-LA and MC-LY bind to the protein kinase serine/threonine AKT3 (a higher affinity of \(-10.7\) kcal/mol to MC-LY than to MC-LA), which regulates metabolism, energy demand, growth, angiogenesis, acts on cell proliferation, and is involved in the up- and downregulation of MMP-13, which is a metastasis activator [30,31]. Clearly, high affinity microcystin variants can alter these processes of important metabolic regulator enzymes, which could explain the proliferation and tumor induction and the indication of apoptosis by these toxins. One of the most studied effects of MCs has been the inhibition of serine/threonine protein phosphatases, especially PP1 and PP2A in their catalytic units PP-1c/PP-2 Ac [32,33]. MCs interact with PPs, resulting in the inhibition effect on adduct formation due to their connection with the catalytic site, the hydrophobic groove, and the C-terminal groove [32,34,35,36]. The inhibition effect is mainly through noncovalent interactions largely by the hydrophobic side chain of the \(\beta\)-amino acid Adda, located at position 5 and the Glu6 residue of the enzyme. This is one of the most important unions of the toxin [5,37]. We found interesting associations with several dual specificity protein phosphatases such as vaccinia H1, dual specificity protein phosphatase 26 (DUSP26), and protein phosphatase 13 (DUSP13). However, all had affinities of approximately \(-9.00\) kcal/mol and insulin-related functions. The effects of MCs on cytoskeleton disruption, cell cycle arrest, metabolic disorders, cell proliferation, and cell death have been linked to PP1/PP2A activity and increased phosphorylation of certain proteins [38].
## 5 Experiments and Results
### Robustness Evaluation
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**\# UAVs**} & \multicolumn{2}{c}{**Fitness**} & \multicolumn{2}{c}{**D\({}_{\text{UAV}}\)**} & \multicolumn{2}{c}{**D\({}_{\text{RD}}\)**} & \multirow{2}{*}{**Formation**} \\ \cline{2-2} \cline{5-8}  & **Mean** & **StDev** & **Mean** & **StDev** & **Mean** & **StDev** & \\ \hline
3 & 3.673 & 0.698 & 5.116 & 0.104 & 3.058 & 0.001 & **100.0\%** \\
5 & 5.602 & 1.032 & 4.765 & 0.030 & 3.070 & 0.002 & **100.0\%** \\
10 & 7.119 & 0.625 & 4.178 & 0.083 & 3.011 & 0.001 & 70.0\% \\
15 & 10.790 & 0.492 & 5.758 & 0.009 & 4.098 & 0.001 & 56.7\% \\
30 & 11.156 & 0.886 & 5.783 & 0.727 & 4.991 & 0.012 & 66.7\% \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Fitness, distance between UAVs, and distance to the rogue drone in metres, all collected from the tests performed on 30 unseen scenarios per case study. Best values are in bold. Figure 7: _Cont_.
# Research on Leaf Area Density Detection in Orchard Canopy Using LiDAR Technology
## 2 Materials and Methods
### Orthogonal Regression Experiment Design  
When using LiDAR for canopy leaf area density detection, various factors can influence the results. These include detection distance, overlapping of leaves within the canopy, leaf distribution, and the magnitude of leaf area density, all of which can affect the scanned point cloud. Directly exploring the relationship between the area of point cloud-fitted leaves and leaf area density during the detection process can introduce certain errors. To mitigate these effects, this research designs a quadratic regression orthogonal experiment to establish a mathematical model for calculating leaf area density. The range of experimental factors is determined based on the target orchard and the operating conditions of plant protection machinery as references. The canopy thickness ranges from 0.2 m to 1.2 m, the leaf area density ranges from 2 m\({}^{2}\)/m\({}^{3}\) to 6 m\({}^{2}\)/m\({}^{3}\), and the detection distance ranges from 1 m to 2 m. The coding table for the three-factor quadratic regression orthogonal design is shown in Table 1 The coding table for the orthogonal composite design of the three-factor quadratic regression is shown in Table 2. To avoid accidental errors in a single measurement, each experimental condition is measured 10 times.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Enodings**} & \multicolumn{3}{c}{**Considerations**} \\ \cline{2-4}  & **Canopy Thickness** & **Detection Distance** & **Leaf Area Density** \\  & \(z_{1}\)**(m)** & \(z_{2}\)**(m)** & \(z_{3}\)**(m\({}^{2}\)/m\({}^{3}\))** \\ \hline \(-\)r & 0.20 & 1.00 & 2.00 \\ \(-\)1 & 0.33 & 1.13 & 2.52 \\ \(0\) & 0.70 & 1.50 & 4.00 \\ \(1\) & 1.07 & 1.87 & 5.48 \\ r & 1.20 & 2.00 & 6.00 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Temary quadratic regression orthogonal factor level coding table.  
Figure 2: Schematic diagram of leaf fitting. The colors in the image represent different clusters formed by point cloud segmentation.
# Fatigue Life Prediction of Radial Tire Bead Using a Maximum Strain Energy Density Range Method
## 2 Fatigue Life Prediction Method  
The fatigue life prediction method applied here is based on the strain energy density range (\(\Delta SED\)) proposed in reference [11]. The \(\Delta SED\) value is defined by the difference between maximum \(\mathit{SED}\) and minimum \(\mathit{SED}\) in the circumferential direction of the bead region. It can characterize the intensity of the energy change for tire bead in the circumferential direction more conveniently, so the strain energy density range is selected as the evaluation index of tire bead fatigue. The \(\Delta SED\) can be expressed as Equation (1).  
\[\Delta SED=\mathit{SED}_{\text{max}}-\mathit{SED}_{\text{min}} \tag{1}\]  
In this study, in order to predict the fatigue life of the tire bead, the steady-state rolling analysis of the finite element method and the fatigue test of the rubber compound in the bead area were used. The specific forecasting steps are as follows:  
1. According to the test standard, carry out the uniaxial tensile fatigue test of rubber compounds, and obtain the fatigue life equations of the curves;
2. Use finite element analysis to obtain the maximum strain energy density range \(\Delta\mathit{SED}_{max}\) value of different compounds at a specific rolling speed in the bead area. \(\Delta\mathit{SED}_{max}\) is the maximum value of \(\Delta\mathit{SED}\) for each compound. It reflects the highest level of energy change in rubber compounds;
3. Calculate the fatigue life of different rubber compounds by inserting the obtained maximum strain energy density range \(\Delta\mathit{SED}_{max}\) value into the fatigue life equation of the rubber compound. The minimum fatigue life of the rubber compound is regarded as the fatigue life of the bead;
4. Use dynamic rolling radius of tire and fatigue life (\(N_{\beta}\)) to calculate the distance traveled at a specific speed.
# The Perpetual Vector Mosquito Threat and Its Eco-Friendly Nemeses
## 5 Eco-Friendly _Lysinibacillus sphaericus_ and _Bacillus thuringiensis_ subsp. _israelensis_
### Brief History of Lysinibacillus (Formerly Bacillus) sphaericus (Ls)
#### 5.1.1 Structural Characteristics of Tpp1 Aa1/Tpp2 Aa1  
Different Tpps have been isolated from Ls, but the three-dimensional (3D) structure of Tpp1 Aa1/Tpp2 Aa1 nanocrystals was resolved at 2.25 angstroms de novo using serial femtosecond crystallography (SFX) at an X-ray free-electron laser [226]. Although their amino acid sequence identity (28%) and similarly (46%) have diverged significantly, Tpp1 Aa1 and Tpp2 Aa1 possess only a few differences at the structural level and have a size of 100 angstroms long and 25-30 angstroms in diameter. Each protein is composed of two domains, a \(\beta\)-trefoil domain, which is involved in carbohydrate- and receptor-binding, and the pore-forming domain, located at the amino- and carboxy-terminals, respectively.  
The main structural difference between Tpp1 Aa1 and Tpp2 Aa1 occurs at the \(\beta\)-trefoil domain, which could be implicated in distinct roles of these proteins during the intoxication process in the midgut epithelia, where Tpp2 Aa1 has a less prominent role in carbohydrate binding when compared to Tpp1 Aa1. The few structural differences between Tpp1 Aa1 and Tpp2 Aa1 indicate they can form a heteromeric pore assembly complex with a topology similar to the aerolysin family of pore-forming toxins [227; 228]. The crystal structure of other Tpps, such as Tpp49 Aa1, Tpp1A2, Tpp80 Aa1, and Tpp35 Ab1, have been elucidated. Although they have a few differences, they maintain similar structures, i.e., the receptor and pore-forming domains [228].  
It must be noted that Ls's Tpp1 Aa1/Tpp2 Aa1 is a single-receptor-specific toxin, the receptor being a GPI-anchored amylomalatase in the midgut epithelial membrane of _Culex_ and _Anopheles_ species Cqm1/Cpm1 and Agm3, respectively, to which advantageous mutations in the host lead to rapid resistance in field populations [223; 224; 225; 226; 227; 228; 229; 230; 231]. Additionally, although a few _Aedes_ species are susceptible to Tpp1 Aa1/Tpp2 Aa1, _Aedes aegypti_ larvae are naturally refractive to Tpp1 Aa1/Tpp2 Aa1 as it lacks a suitable midgut receptor [232]. Therefore, Ls's use in IPMPs is inconsequential to this species.