esa-sceva/satcom-chunk-collection huggingface dataset:
# BVLOS Unmanned Aircraft Operations in Forest Environments
## 5 Taking a Risk-Based Approach to BVLOS Operations in Forest Environments
### Technological Considerations
#### 5.1.3 Lidar  
Lidar (acronym for light detection and ranging) is a tool widely used within forestry for remote sensing and data collection, particularly for measuring tree height and collecting inventory data [51; 52]. Lidar has a much shorter wavelength compared with primary radar, which means that its effective range is also shorter, though with better range and azimuth accuracy [41]. However, for low-level flying, lidar is capable of identifying obstacle collision hazards (e.g., trees and vegetation, powerline cables and manmade structures, animals, etc.) and helping to maintain the unmanned aircraft at a certain separation above the ground [41; 53]. Lidar's limited effective range is approximately 50-100 m [54], so it is not an effective DAA tool against other aircraft. Rather, its application as a DAA is usually in a complementary role by providing low-level detection of obstacles. Nonetheless, recent research has shown that unmanned aircraft can perform autonomous sub-canopy flights using only lidar technology to avoid obstacles along the flight path [55; 56]. SLAM-based UAV DAA systems have the dual benefit of being able to capture a detailed three-dimensional model of the forest beneath the canopy, which can be used for stand analysis [57]. SLAM algorithms also work by matching geometric features from their environment to aid in situational awareness and navigation, so a forest is arguably an ideal situation for their use, as tree stems provide a feature-rich environment [58]. This shows great potential for future research as lidar is a relatively inexpensive DAA tool if used in this way. The flight is sub-canopy, which means that the airborne risk is zero because no manned aircraft can enter the operating area, offering potentially more accurate data for issues, such as sub-canopy snow depth or sub-canopy invasive species, than current methods that use unmanned aircraft to fly above the canopy [59; 60]. Nonetheless, the corollary is that flight at lower altitudes means that less area can be covered than using overflight, so the viability of this BVLOS technique is highly dependent on the size of the forest environments and the quality of data that is required.
## 2 Related Work and Foundational Concepts
### Software and System Development Life Cycles
#### 2.1.3 V-Model  
The V-Model, an extension of Waterfall, emphasizes verification and validation by pairing each development phase with a corresponding testing phase [13,14]. This structured approach is beneficial for projects where reliability and rigorous testing are paramount. However, like Waterfall, its rigidity can be a significant limitation, hindering rapid adaptation to new security threats or regulatory changes, making it less suitable for highly dynamic or classified systems [4].
# A Crowdsensing Platform for Monitoring of Vehicular Emissions: A Smart City Perspective
## 6 Results and Discussions  
### What Are the Sensors Supported by Each Vehicle?  
The first analysis was to evaluate the practical feasibility regarding the extraction of data from the vehicular sensors. It is known that the OBD-II has access to six groups of variables (control, motor, fuel, pressure, temperature, and others), in order to guarantee the analysis of vehicle components (sensors), but not all vehicles support all sensors. The objective of these tests was then to verify the availability of the MAF and MAP sensors in the sample. Thus, for the vehicles in the defined sample set, the available sensors are shown in Table 5.  
In Table 5, it can be observed that most of the considered vehicles had the MAP sensor. On the other hand, for the sample studied, the MAF sensor was in a minority of cars, and none of them had both of the sensors. Some factors that may influence this result is the vehicle manufacturing year or even the optional items that the driver may have opted to include or not in the act of purchasing.  
After the verification of the practical feasibility regarding the available sensors in the vehicle sample set, we can start to answer Question 2.
## 3 On-Site Test Results and Analysis
### Analysis of the Midspan Section Acceleration for the Largest Span
Figure 9: FFT time\(-\)frequency diagram of vertical acceleration during the vehicle-induced phase of the bridge deck in Section C: (**a**) waveform diagram; (**b**) single peak spectrum. coupling effects. Free vibration directly reflects the inherent frequency differences, while forced vibration amplifies these differences due to the dynamic response characteristics and the interaction with the excitation frequency. In practical engineering applications, it is essential to clarify the frequency characteristics of each component through modal analysis or experimental testing to optimize the vibration damping design. The acceleration response at the rail and deck of the continuous girder in Section C significantly increases at 245 km/h. The corresponding train loading frequency is about 2.7 Hz, which approaches the lowest dominant frequency of 2.5 Hz for the time-frequency analysis of the deck signal, and 2.5 Hz can be considered the first-order vertical intrinsic frequency of the continuous girder. The first-order vertical inherent frequency of the structure analyzed by ANSYS is 2.321 Hz, and the difference between the calculated and experimental results is 7.71%, indicating that the finite element model predicts reasonably.
## 2 Literature Review and Methodology
### Modern Estimation Methods
Then, the unscented transformation approach is used, which selects a set of sigma points (X) which are near the current state (\(\hat{x}\)). The unscented transformation is the approach to use probability distribution to calculate a random variable data which undergoes non-linear transformation. While the Jacobian uses a single specific point of the non-linear function to find its linear relationship, the sigma points use multiple points to find the non-linear function linear relationship [7, 19]. To address potential inaccuracies in sensor data, the proposed BMS incorporates several error mitigation strategies. Sensor fusion techniques are employed to combine inputs from multiple sensors, cross-validating data and filtering out inconsistencies. Additionally, the Kalman filter's process and measurement noise covariance matrices are carefully tuned to model typical sensor inaccuracies and random noise effectively. These measures enhance the robustness of the SOC and SOH estimations. Diagnostic algorithms are also implemented to detect anomalies in sensor readings, triggering recalibration or switching to simpler estimation methods, such as Coulomb counting, when needed.
## 4 Microbial Adhesion
### Control of Bacterial Adhesion
#### 4.5.3 Anti-Adhesion/ Bacteria Repelling Coatings  
The coatings aim to prevent the first step in biofilm formation using non-cytotoxic mechanisms, in contrast to the other two forms of antibacterial coatings. Bacterial attachment to the surface occurs through several mechanisms and, if not prevented in the first stage, biofilm formation can lead to serious infections. The control over surface hydrophobicity, charge, roughness, and topographical configuration can drastically reduce the attachment of bacteria.  
Poly Ethylene Glycol (PEG) is the most commonly used anti-adhesion coating. PEG chains are pliant and they display steric aversion forces, which hinder the bacteria attachment in the surface [82]. PEG was first used for antifouling coatings against protein absorption, and due to its preventative properties was applied for preventing bacterial adhesion. Ostuni et al. investigated whether the protein and bacteria resistance correlates to an extension to each other [83]. He observed that there was no correlation between the two, and therefore presumed that other parameters are required to prevent the attachment of bacteria, due to the complexity of the mechanisms that result in bacterial attachment. Furthermore, the PEG was not sustainable for long term usage [84].  
Other anti-adhesion coatings make use of surfactants to prevent bacterial attachment. Hydroxyapatite and chitosan or biological molecules are examples of this surfactant, such as albumin and heparin [85]. In a recent study that was performed by Buzzacchera et al., polymer brush functionalized chitosan was used as an antifouling implant coating to prevent bacterial adhesion and diminishing the platelet activation and leukocyte adhesion [86]. The coating was hydrophilic and it reached a thickness of up to 180 nm within 30 min. of polymerization. Another approach for anti-adhesion coatings is based on the hydrophobicity where the superhydrophobic or hydrophilic coatings aggravate the attachment for bacteria to the surface. A more recent study uses a repellent film that is based on flagellin [87]. In this study, a novel type of bacteria repellent layer that was made of the flagellin is examined. This concept showed that the flagellin protein forms an oriented and dense layer on a hydrophobic surface, which resulted in an effective bacterial cell repellent feature. The cost-effectiveness is the big advantage of fabricating flagellin based coatings, because the protein can be rather easily produced in large amounts.
###### Abstract  
This work presents a study on the preparation of plasma-polymerized aniline (pPANI) nanofibers and nanoparticles by an intense plasma cloud type atmospheric pressure plasma jets (iPC-APPI) device with a single bundle of three glass tubes. The nano size polymer was obtained at a sinusoidal wave with a peak value of 8 kV and a frequency of 26 kHz under ambient air. Discharge currents, photo-sensor amplifier, and optical emission spectrometer (OES) techniques were used to analyze the plasma produced from the iPC-APPI device. Field emission scanning electron microscopy (FE-SEM), transmission electron microscopy (TEM), Fourier transform infrared spectroscopy (FT-IR), gas chromatography-mass spectrometry (GC-MS), and gel permeation chromatography (GPC) techniques were used to analyze the pPANI. FE-SEM and TEM results show that pPANI has nanofibers, nanoparticles morphology, and polycrystalline characteristics. The FT-IR and GC-MS analysis show the characteristic polyaniline peaks with evidence that some quinone and benzene rings are broken by the discharge energy. GPC results show that pPANI has high molecular weight (\(M_{w}\)), about 533 kDa with 1.9 polydispersity index (PDI). This study contributes to a better understanding on the novel growth process and synthesis of uniform polyaniline nanofibers and nanoparticles with high molecular weights using the simple atmospheric pressure plasma polymerization technique.  
polyaniline; atmospheric pressure plasma; nanofiber; nanoparticle; plasma-polymerized aniline (pPANI); transmission electron microscopy (TEM); polycrystalline; intense plasma cloud type atmospheric pressure plasma jets (iPC-APPI) +
Footnote †: journal: materials  
Article
## 2 Materials and Methods
### Laboratory Experiments
#### 2.2.2 Moisture Retention Tests  
The ability of a biopolymer to enhance the soil moisture retention capacity is one determinant of its potential as a dust suppressant. Soil with increased moisture retention capacity can bind water over longer periods and slow the evaporation effect [47]. Moisture makes soil particles heavier and enhances the interparticle binding force [17], causing it to be less susceptible to being suspended in the atmosphere by erosive forces.  
For the moisture retention tests performed in this study, the weights of the samples were recorded before and immediately after the biopolymer application. The treated samples were then cured in the laboratory (RWTH Aachen, spring) for four days (96 h) at ambient temperature (21 \(\pm\) 1 \({}^{\circ}\)C) and humidity (45 \(\pm\) 2.5%), their weights were recorded every 24 h (t = 0, 24, 48, 72, and 96 h post application). The moisture retention, \(\omega\) (wt%),  
Figure 2: (**a**) Weighing of dry sample, (**b**) preparation of biopolymer solution, (**c**) gravimetric spray-on application of biopolymer solution with splash guard.  
was calculated as the proportion of the initial moisture applied at t = 0 h that was retained in the sample at t = 96 h and was calculated according to Equation (1):  
\[\omega=\frac{m_{1}-m_{dry}}{m_{0}-m_{dry}}\times 100 \tag{1}\]  
where \(m_{1}\) denotes the sample weight after 96 h of curing (g), \(m_{0}\) is the sample weight after application of the biopolymer solution (g), and \(m_{dry}\) represents the dry sample weight prior to the application of the biopolymer solution (g).
## 3 Problem Formulation
### Optimization Problem Formulation
#### 3.1.1 Maximization of Number of Served Users  
The number of served users by the UAV, \(n_{c}\) is supposed to be maximized at each time slot. Let \(F\) be the NFZ and a nonself-intersecting convex quadrilateral that is defined by its vertices as \(\vec{V}_{i}=(x_{i},y_{i},z_{i})\), where \(i=1,2,3,4\). Moreover, let \(\vec{C}_{3d}\in\mathbb{R}^{3}\) be a vector containing the 3-dimensional (3-D) coordinates of the UAV, and \(C_{2d}\) be a point in \(xy\)-plane, representing the projection of the UAV on the \(xy\)-plane, and imagine we draw straight lines from each vertex of \(F\) to the point \(C_{2d}\). Then, the optimization problem can be modeled as follows:  
\[\begin{split}\underset{\vec{C}_{3d}\in\mathbb{R}^{3}}{\text{max }}& f(\vec{C})\\ \text{s. t.}& C_{1}\textbf{: }& h_{\text{min}}\leq h_{d} \leq h_{\text{max}},\\ & C_{2}\textbf{: }&\angle A_{1}+\angle A_{2}+\angle A_{3}+ \angle A_{4}<2\pi,\\ & C_{3}\textbf{: }&\theta<\pi,\\ & C_{4}\textbf{: }& P_{1}\leq h_{\text{max}},\end{split} \tag{20}\]  
where \(\angle A_{1}=\angle V_{1}C_{2d}V_{2}\), \(\angle A_{2}=\angle V_{2}C_{2d}V_{3}\), \(\angle A_{3}=\angle V_{3}C_{2d}V_{4}\), and \(\angle A_{4}=\angle V_{1}C_{2d}V_{4}\), \(f:\mathbb{R}^{3}\rightarrow\mathbb{R}\) is the objective function, and \(f(\vec{C})=n_{c}\) in this case.  
Explanations of Constraints in (20)  
* \(\textbf{C}_{1}\)**:** The altitude of the UAV (\(h_{\text{d}}\)) is regulated in many countries and regions, such that the maximum (\(h_{\text{max}}\)) and minimum (\(h_{\text{min}}\)) altitudes that UAVs can flight are determined. Therefore, in this work, the UAV is supposed to obey these limitations in terms of the altitude.
* \(\textbf{C}_{2}\)**:** Since \(F\) is defined as the NFZ, it means that the UAV BS cannot fly over it. As such, this constraint confirms that the UAV BS is flying out of \(F\), such that the projection of the UAV BS on the \(xy\)-plane, \(C_{2d}\), is not within \(F\).
* \(\textbf{C}_{3}\)**:** The directivity angle of the antenna of the UAV BS can be \(\pi\) at maximum (The use of an isotropic antenna is not a good idea for UAV BSs as they are serving to the usersunder them in terms of height, and there is no sense to provide a radiation above the UAV BS. Therefore, we assume that the maximum antenna angle for the UAV BSs should be \(\pi\)), but practically it should be less than that to have a better antenna gain. Though this could be normally not a hard constraint, in this work we deal with the case where the antenna angle is less than \(\pi\), thereby this becomes a constraint for the optimization problem.
* \(\mathbf{C_{4}}\): Given that the maximum transmit power of the BSs are regulated, this constraint captures such regulations, meaning that the transmit power of the UAV BS has an upper bound.
# Efficient Position Change Algorithms for Prolonging Driving Range of a Truck Platoon
## 3 The Position Change Problem and the Algorithms
### Reduction of Position Changes  
As noted before, if we decrease \(\delta\), the vehicles need to change their position within the platoon more frequently. Since the position change operation needs a change of speed of vehicles, which might cause extra consumption of fuel, it is very desirable to reduce the number of position changes. To address this ongoing problem, we turn our focus to reducing the number of position changes while achieving the same driving range as before.  
The intuition of reduction in the number of position changes comes from the position change matrix \(P\). For two time slot ids \(k_{1}\) and \(k_{2}\), if \(P(i,k_{1})=P(i,k_{2})\) for all \(i\in V\), we swap the time slot \(k_{1}+1\) or \(k_{1}-1\) with the time slot \(k_{2}\). Thus, two consecutive time slots maintain the same position orders, which reduces the position change for the two time slots. As an example, let us consider a position change matrix \(P\) shown in Table 2.  
In this example, the number of vehicles is \(n=4\) and the number of time slots is 5. There are same patterns of position arrangements when \(k=0\) and \(k=3\), and \(k=1\) and \(k=4\). In this case, we can reorder the time slots so that we have Table 3, which may have fewer position changes.  
Before we discuss the algorithms to reorder a position change matrix, we need to define the definition of position changes. We come up with two different definitions of position changes. One way is to count the position changes vehicle-wise. When there is any position change for a vehicle in the platoon, every position change is counted as one change. For example, in Table 3, the positions of vehicle 1 changes from 1 to 2, and 2 to 3. Thus, vehicle 1 has two position changes. Similarly, vehicles 2, 3, and 4 have \(2,1,0\) position changes, respectively. So the total number of position changes is 5.  
Another way is to count the position changes time slot-wise. It is considered as one position change only if there is any position difference between two consecutive time slots. In Table 3, we only have two position changes: one between \(k=1\) and \(k=2\), and the other between \(k=3\) and \(k=4\). In this paper, we use the time slot-wise definition where it has only two position changes from \(k=1\) to \(k=2\) and from \(k=3\) to \(k=4\) in Table 3, compared to the case of Table 2, which has four position changes.  
So the remaining question is how to find a good reordering of time slots of \(P\). We propose a simple sort based algorithm called \"Sorted Time Slot (STS)\". STS sorts the columns of \(P\), which is generated by running the algorithm TTH described in Algorithm 1, in lexicographical order. The sorting operation in STS results in moving duplicate columns into consecutive columns, thus reducing the number of position changes. Actually, Table 3 is derived by running STS for the position change matrix \(P\) in Table 2. Since the algorithm is quite simple and intuitive, we do not elaborate further.
# An Evaluation of Optimization Algorithms for the Optimal Selection of GNSS Satellite Subsets
## 2 GNSS Satellite Constellation Determination  
### Weighted Geometric Dilution of Precision (WGDOP)  
The accuracy of the obtained GNSS position depends on satellite geometry (GDOP) and user range equivalent error (UERE) [21; 22]. The UERE indicates the quality of satellite measurements (pseudo-range), and it is affected by the satellite signal power [33; 34], which can be expressed by the value of signal-to-noise ratio (SNR) and carrier-to-noise ratio (CNR) [34]. The value of SNR and CNR increases with the strength of a satellite signal [35]. Since the GNSS's positioning accuracy relies on GDOP and UERE, satellites should be selected based on them. However, calculating the UERE value can be more complex compared to the values of signal power (SNR/CNR), which are directly calculated by the receiver. Thus, the optimal satellite subsets were selected based on the satellite geometry (GDOP) and signal power (CNR) using _WGDOP_. Similar to GDOP, the value of _WGDOP_ decreases with the improvement in the geometry of the satellite constellation, and it can be computed as follows:  
\[WGDOP=\sqrt{trace(H^{\mathrm{T}}WH)^{-1}} \tag{1}\]  
\[H=\begin{bmatrix}cosE_{1}sinA_{1}&cosE_{1}cosA_{1}&sinE_{1}&1\\ cosE_{2}sinA_{2}&cosE_{2}cosA_{2}&sinE_{2}&1\\ \vdots&\vdots&\vdots&\vdots\\ cosE_{n}sinA_{n}&cosE_{n}cosA_{n}&sinE_{n}&1\end{bmatrix} \tag{2}\]\[W=\begin{bmatrix}w_{1}&0&\dots&0\\ 0&w_{2}&\dots&0\\ \dots&\dots&\ddots&\dots\\ 0&0&\dots&w_{n}\end{bmatrix} \tag{3}\]  
where \(H\) is the geometry matrix determined based on the elevation angle, \(E_{n}\), and azimuth, \(A_{n}\), of the \(n_{th}\) satellite, and \(W\) is the weighting matrix, where \(w_{n}\) is the weight of the \(n_{th}\) satellite. According to [PERSON] et al. (2020) [22], the weight \(w_{n}\) is defined as the SNR of the \(n_{th}\) satellite. However, the CNR was used instead of the SNR in the current study because it was obtained from the receiver. The WGDOP based on the CNR will be called CNR-WGDOP.
## Dynamic Mechanical Behaviors of Desert Sand Concrete (DSC) after Different Temperatures  
[PERSON] **1**, [PERSON] **1,*, [PERSON] **1**, [PERSON] **1** and [PERSON] **2,***  
1 College of Civil and Hydraulic Engineering, Ningxia University, Yinchuan 750021, China; [EMAIL_ADDRESS] (M.Z.); [EMAIL_ADDRESS] (S.S.); [EMAIL_ADDRESS] (X.C.)
2 Faculty of Civil Engineering and Earth Resources, University Malaysia Pahang, Gambang 26300, Pahang, Malaysia
* Correspondence: [EMAIL_ADDRESS] (H.L.); [EMAIL_ADDRESS] (S.I.D.); Tel.: +86-152-0268-3618 (H.L.)  
Received: 3 September 2019; Accepted: 30 September 2019; Published: 3 October 2019  
Featured Application: This paper applies to solve the shortage of building sand on the basis of principle of sustainable development. Meanwhile, this paper also provides guidance for desert sand concrete (DSC) subjected to elevated temperature and dynamic loading.  
**Abstract:** In the building domain, the non-renewable resource of sand is widely used to produce concrete and mortar. The sand production has been estimated to be more than 10 billion tons with a total of 1.2 billion tons used in concrete in the last decade, which causes the gradual reduction of available building materials and impacts the environment. Since there are abundant desert sand resources in northwestern China, it would be viable to utilize desert sand as an alternative material for concrete production. In this study, an investigation of dynamic mechanical behaviors of desert sand concrete (DSC) was conducted. Various desert sand replacement ratios (0-100%) were used to replace the equivalent hill sand as fine aggregate. Experimental results showed that strain rate had a strong effect on the dynamic mechanical behaviors of DSC. The compressive strength (at room temperature) and flexural strength (after elevated temperature) increased with desert sand replacement ratio (DSRR) with the optimum replacement ratio of 40%, which was because the increase of DSRR improved the compaction of DSC. However, the effect of the low strength of desert sand was higher than that of the compaction when the DSSR exceeded 40%, so both strength values generally decreased with the increase of DSRR. Moreover, the dynamic constitutive model of DSC at room temperature was established on the basis of a nonlinear visco-elastic constitutive model (ZWT model), which can predict the stress-strain curves of DSC.  
**Keywords:** desert sand concrete (DSC); dynamic compressive strength; dynamic flexural strength; deflection; strain rate; temperature