esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Applications of Microextraction Techniques to the Determination of Organic Micropolultants
### Pharmaceuticals
The former produced significantly higher extraction efficiencies for polar analytes (% recovery values near 100% for most of the studied analytes) than the commercial product. The LODs of the developed method were 10 ng\(\cdot\)L\({}^{-1}\) for most of the target compounds, with the exception of naproxen (50 ng\(\cdot\)L\({}^{-1}\)). Similarly, the same authors [95] prepared a stir bar coated with a hydrophilic polymer based on poly(N-vinylpyrrolidone-co-divinylbenzene) for the sorptive extraction of polar pharmaceuticals from environmental water matrices, including river, effluent and influent waste water, followed by liquid desorption and subsequent LC-MS/MS. The LODs were between 10 and 50 ng\(\cdot\)L\({}^{-1}\). Finally, a new polar monolith based on poly(poly(ethylene glycol) methacrylate-co-pentaerythnitol triacrylate) (poly(PEGMA-co-PETRA)) was synthesised by [PERSON] _et al._[96] and applied as a coating for stir bar sorptive extraction (SBSE) to determine a group of pharmaceuticals from environmental water samples. The coating could extract and desorb most of the studied analytes more effectively and rapidly than the recently commercialised polar stir bars. The analytical methodology was validated with LODs between 15 and 50 ng\(\cdot\)L\({}^{-1}\). [PERSON] _et al._[97] compared SBSE and DLLME methods; both techniques were used with acetone as the dispersant and chlorobenzene as the extractant solvent to analyse statin drugs in different environmental water samples. DLLME generated better results than SBSE because SBSE only extracted two of the six pharmaceuticals. The DLLME recoveries approached 92%. Different researchers have used DLLME to determine pharmaceutical compounds with LC-MS. [PERSON] _et al._[98] developed an ultrasound-assisted ionic liquid dispersive liquid-liquid microextraction (US-IL-DLLME) procedure for the extraction of nine pharmaceuticals from wastewater samples. The US process accelerated the formation of a fine cloudy solution containing an ionic liquid (IL), specifically 1-octyl-3-methylimidazolium hexafluorophosphate ([C\({}_{8}\)MIM][PF\({}_{6}\)]), and acetonitrile (ACN) as the extractant and dispersant, respectively. Moreover, the recoveries of the pharmaceuticals increased when an ice-water bath extraction was included during the analytical procedure. The LODs for the extraction of the target analytes from wastewater samples ranged from 0.2 to 60 ng\(\cdot\)L\({}^{-1}\) with recoveries between 88% and 111%.
# Implicit-Explicit Coupling Enhancement for UAV Scene 3D Reconstruction
## 3 Experiments  
To demonstrate the effectiveness of our method, we conducted experiments on five different scenes, including urban, rural, countryside, mountain, and campus scenarios. The test data consisted of multiscale aerial images, covering areas of several square kilometers. During the data collection with UAVs, the UAV model used was DJI Mavic 2. We conducted aerial operations at an altitude of 120 m, capturing photographs at a 30 deg angle. The final image resolution was 4000 deg3000. We employed a ground station for UAV positioning to ensure an image overlap rate exceeding 85%. The data included regions with rich textures (buildings), repetitive textures (roads and vegetation), and non-textured areas (water). Additionally, since the collected data were not acquired simultaneously, they had variations in lighting conditions, making precise 3D reconstruction of the entire dataset challenging. We evaluated the dataset from multiple perspectives. In the implicit modeling phase, we compared our method to some advanced techniques (NeRF [28], TensorRF [41], Mega-NeRF [33]). In the explicit modeling phase, we compared our results to those of various existing aerial modeling software (including COLMAP 3.9, Context Capture 2023, PhotoScan 2023, Pix4D 4.5.6).
## 2 Structures of Basic Spoke Type IPM Model and Proposed Flared Type IPM Model
### Proposed Flared Ferrite IPM Model  
In this paper, we propose a new model with a flat rotor shape to replace the existing model and improve performance. The flared-shape structure has several C-shaped ferrite magnets arranged in a flared shape in the rotor. The flared-shape structure of the ferrite magnets maximizes the utilization of the ferrite magnets in the rotor core. Thus, the flared type structure can concentrate the magnetic flux, which is caused by the arrangement of the magnets.  
Figure 7 shows the structure of the proposed flared type IPM model, which has 12 slots /8 poles and concentrated winding, and a permanent magnet is magnetized in a concentrated flux structure (white mark). One pole consists of four C-shaped magnets, which are arranged in a flared shape. Figure 8 shows the design process of IPM motors. In this paper, the permanent magnet weight of the proposed model is set to be the same as that of the basic model, and then a flared magnet arrangement structure is designed for the rotor.  
Figure 5: Nodal force components of the stator teeth.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Item** & & **Unit** & **Basic Model** \\ \hline \multirow{2}{*}{Nodal force (peak to peak)} & x-axis & Nm & 10.93 \\  & y-axis & Nm & 121.24 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Nodal force component in x-axis and y-axis directions.  
Figure 6: Nodal force of the stator teeth (absolute component).  
Figure 9 shows the magnetization pattern of the proposed flared type IPM model. As shown in Figure 9a,b, the proposed flared type IPM model can have two different patterns (parallel and radial directions) and the magnetization pattern of the permanent magnet can determine the manufacturing process of the permanent magnet. The magnetic flux density distribution inside the rotor resulting from each magnetization pattern is different, as shown in Figure 9c,d, and the radial magnetization pattern has the advantage that the magnetic flux is more concentrated than in the parallel magnetization pattern. In this paper, characteristic analysis was performed on two patterns to determine the pattern of magnetization. Finally, we applied the radial magnetization pattern to improve performance and reduce torque ripple. Table 4 shows the magnet specifications of the conventional spoke model and the proposed flared model. As shown in Table 4, the total use of permanent magnets in the initial model and in the proposed model is the same. This means that there is no increase in material cost due to permanent magnets. Although, the proposed flared type rotor structure is composed of a number of  
Figure 8: Design process of IPM motor.  
Figure 7: Structure of the proposed flared ferrite IPM model (1/4 model).  
magnets compared to the basic IPM model so the assembly cost can increase in the manufacturing process. However, it can be improved by using the assembly automation technology.
## 2 Related Work
### Spatial Distribution Information Content Measurement of Area Map  
Spatial distribution information in area maps refers to the information represented by the spatial location, topological relationships, distance relationships, and other characteristics of area objects within the map space [4; 26]. Existing methods extract spatial distribution relationships by constructing topological graph structures based on the centroids of area objects [14; 15] or spatial adjacency graph structures based on the Voronoi diagram of area objects [16; 17]. These graph structures are then used to measure the spatial distribution information of area maps. As area maps encompass both continuous and discrete areal objects, the approach of using Voronoi diagram tessellation to construct spatial adjacency graph structures is more widely applicable.  
In existing studies, [PERSON] et al. have measured the spatial distribution information of area maps by using the adjacency degree of area objects and the difference in types of adjacent area objects as metrics [16]. [PERSON] et al., on the other hand, have expressed the distance relationship between area objects through the size of their Voronoi diagram areas, and have expressed the spatial distribution relationship using the adjacency degree of area objects [17]. These metrics are then weighted and combined to comprehensively measure the spatial distribution relationship.  
[PERSON]'s method considers the differences in types of area objects in their distribution patterns. However, in practical applications, there may be instances where all area objects belong to the same type, which limits the applicability of the information measurement model. Furthermore, this measurement model fails to consider the spatial configuration relationships between adjacent area objects and can only provide quantitative statistics, resulting in incomplete information measurement. Finally, [PERSON]'s study requires specific regulations on the size of area maps, which restricts the universality of spatial information measurement.  
In [PERSON]'s method, the author employs the size of Voronoi diagram areas to represent the distance relationships between area objects. However, there may be heterogeneity in the diagram, where the distance relationships between area objects and their adjacent objects exhibit heterogeneity. The use of a uniform indicator to characterize the distance relationships neglects the diversity of spatial distance relationships among area objects. Secondly, the author constructs Voronoi diagrams by extending the area maps outward by 30%. This approach leads to inconsistencies in the extension range for different map sizes,resulting in significant fluctuations in the Voronoi diagram areas of objects near the map boundaries. This, in turn, affects the stability and consistency of the measurement results.
# A Voxel-Based Watermarking Scheme for Additive Manufacturing
## 2 Materials and Methods
### Digital Model Watermarking
#### 2.3.1 Iterative Correspondence Training  
The correspondence training process is conducted as follows. Initially, each lattice node is given a random weight vector \(w\) = (\(w_{x}\), \(w_{y}\), \(w_{z}\)). Then, at each iteration, a voxel is randomly selected from the ROI. Assume this voxel is indexed by \(I=(i,j,k)\) in the AABB. At the following step, the lattice node, whose weight vector \(w\) is most similar to \(I\), is searched. This node is the _winner_ node and its weight vector is revised by  
\[w(t+1)=w(t)+\delta(t)(I-w(t)),\,0\leq\delta(t)\leq 1. \tag{3}\]  
Figure 4: The SOM scheme uses a 2D lattice (**middle**) to bridge the watermark (**left**) and the ROI (**right**). The lattice nodes are trained to form a network of correspondences. Then, the watermark is inserted into the ROI via these correspondences.  
Figure 3: (**a**) the front and top views of the ROI, shaded in light-blue, (**b**) the watermark in the ROI, (**c**) the extracted watermark image.  
where \(\delta(t)\) is a learning factor, shrinking with time \(t\). After the weight vector of the winner is revised, the weight vectors of its neighbors within the _vicinity_ are also modified as follows,  
\[w_{j}(t+1)=w_{j}(t)+\gamma\cdot\delta(t)(I-w_{j}(t)),\ 0\leq\gamma\leq 1,\ \gamma\propto \frac{1}{d_{j}+0.5}. \tag{4}\]  
where \(w_{j}\) is the weight vector of the \(j\)-th neighbor, \(d_{j}\) is the distance between the winner and this neighbor, and \(\gamma\) is a scaling factor proportional to the inverse of \(d_{j}\). The vicinity is defined by a circle, centered at the winner node. Its radius is shrunk with time to ensure the convergence of the SOM. The above training procedure repeats until the weight vectors of all the lattice nodes converge or the number of iterations exceeds a predefined limit. The fundamental principles of SOM can be found in the researches of [24; 25].
# Passive Harmonic RFID System for Buried Assets Localization
## Harmonic Tag  
### Frequency Bands and Regulations  
A passive harmonic tag is based on a non-linear device (usually a diode) that generates a useful emission at the second harmonic of the reader's fundamental frequency. As in all passive RFID systems, higher transmitted power makes harmonic tag detection easier. The options for high power license-free operation are typically chosen between the 865/915 MHz, 2.4 GHz, or 5.8 GHz bands. In order to reduce losses due to the soil, the lowest frequency band is preferable. Therefore, the UHF RFID band at 865-868 MHz is chosen.  
From a regulation point of view, one potential drawback is the interference of the second harmonic generated by the harmonic tag. The second harmonic (1730-1736 MHz in Europe or 1810-1830 MHz when 915 MHz is used) falls into the mobile Long Term Evolution (LTE) band 3. As the power received at the tag is relatively small, conversion loss is high. The result is that the second harmonic falls below the emission mask, as the typical license-free emission limit (FCC Part 15.209, in the United States) in which the effective isotropic radiated power (Equivalent Isotropic Radiated Power, EIRP) is \(-41.2\) dBm at a distance of 3 m. In Europe, the ETSI standard EN 302 208 considers that the tag response is a spurious emission that must be below \(-47\) dBm (Equivalent Radiated Power, ERP) at 1 m of distance. Additionally, for the United States, the FCC has determined that such tags (like RFID) are passive devices and the certification is done on the reader where the second harmonic can be easily filtered. Also, spread spectrum techniques used in modern mobile systems help to mitigate the effect of potential narrowband interference. Moreover, as the tag is interrogated from the reader using a very strong signal, typically \(-20\) dBm or more to generate a useful return, any external interference over the reader transmitted signal would be negligible. It is also sometimes forgotten that conventional UHF tags work as non-linear elements, also generating spurious emissions [40; 41], but fortunately, they do not interfere with the systems that operate in the mobile communications band due to their low level. This point will be discussed later according to the experimental results.
## 3 SCH Nonlinear Mathematical Model (NMM)  
### Modeling According to First Physical Principles  
#### 3.1.1 Main Rotor Model  
To build a whole dynamics model, a method with suitable complexity must be chosen. For this 2-blade single-rotor small-scale helicopter, the complex rotor dynamics can be simplified while maintaining sufficient accuracy. First, a uniform inflow model was adopted and the induced velocity \(v_{imr}\) was iteratively computed with the rotor lift [21]. A coupled first-order tip-path-plane (TPP) equation in roll and pitch motions was applied [12]:  
\[\begin{split}\tau_{f}\dot{a}&=-a-\tau_{f}q-\frac{1} {\Omega_{mr}}p+\frac{8}{\gamma\Omega_{mr}^{2}}\frac{K_{\beta}}{I_{\beta}}b+ \theta_{1s}\\ \tau_{f}\dot{b}&=-b-\tau_{f}p-\frac{1}{\Omega_{mr}}q+ \frac{8}{\gamma\Omega_{mr}^{2}}\frac{K_{\beta}}{I_{\beta}}a+\theta_{1c}\end{split} \tag{2}\]  
where \(\tau_{f}\) is the rotor time constant, \(\gamma\) is the rotor Lock number, \(K_{\beta}\) is the spring constant of the flapping configuration and \(I_{\beta}\) is the blade moment of inertia about the flapping hinge. \(a\) and \(b\) are the longitudinal and lateral flap angles.  
\[\tau_{f}=\frac{16}{\gamma\Omega_{mr}} \tag{3}\]  
\[\gamma=\frac{\rho c_{mr}a_{0}R_{mr}^{4}}{I_{\beta}} \tag{4}\]  
where \(a_{0}\) is the lift coefficient slope of blade. In hover condition, \(\gamma\) can be corrected for the inflow effects [13]. The effective Lock number \(\gamma_{eff}\) is  
\[\gamma_{eff}=\frac{\gamma}{1+a_{0}s/16\lambda_{i}} \tag{5}\]  
where \(s\) is the rotor solidity and \(\lambda_{i}\) is the non-dimensional inflow ratio, \(\lambda_{i}=\sqrt{C_{T}/2}\). The rotor force and moment can be simplified using the TPP equation.  
Figure 4: The weight distribution of Configuration WD.  
\[X_{mr}=-T_{mr}\sin a\cos b\] \[Y_{mr}=T_{mr}\sin b\cos a\] \[Z_{mr}=-T_{mr}\cos a\cos b \tag{6}\] \[L_{mr}=(H_{mr}T_{mr}+K_{\beta})\sin b\] \[M_{mr}=(H_{mr}T_{mr}+K_{\beta})\sin a\] \[P_{mr}\!=\!-N_{mr}\Omega_{mr}\]  
where \(X\), \(Y\) and \(Z\) are forward force, side force and upward force, respectively. \(L\), \(M\) and \(N\) are roll moment, pitch moment and yaw moment, respectively. \(T_{mr}\) is the rotor thrust, calculated by the blade element method. This simplification (Equation (6)) is credible for \(\mu<0.3\) (equivalent to \(u<42.7\) m/s for SCH) [21]. According to our flight condition, the simplification is trustworthy. The rotor rotation speed \(\Omega_{mr}\) was fixed by the Krosmik ESC to 1700 rpm during the flights. \(N_{mr}\) was calculated by the integration of blade elements' drag [22].
## 3 Problem Description and Formulation
### Formulation
#### 3.2.1 Cost Analysis  
This study not only aims to minimize the total travel distance, but also the costs caused by carbon emissions. The objective is to reach a trade-off among the number of vehicles used and travel costs and carbon emissions. The comprehensive costs include fixed cost, travel cost, and carbon emission cost produced by the special vehicles that deliver hazardous chemicals.  
Fixed cost is dependent on the number of special vehicles used including the depreciation of special vehicles and the drivers' salaries. Considering f transportation frequencies for special vehicles \(v\in V\), the fixed cost of special vehicles is computed as:  
\[C_{1}=\sum_{f\in F}\sum_{v\in V}\sum_{j\in N\setminus\{0\}}c_{v}x_{0 jv}^{f} \tag{1}\]  
where \(c_{v}\) is the fixed cost of special vehicles \(v\).  
Travel cost is dependent on travel distances during the delivery process. The travel cost is computed as follows:  
\[C_{2}=\sum_{v\in V}\sum_{f\in F}\sum_{(i,j)\in A}w_{v}d_{ij}x_{ijv}^{f} \tag{2}\]  
where \(w_{v}\) is the variable cost of special vehicles \(v\).  
The carbon emission cost is similar to the cost of CO2 emissions during the delivery [30]. This includes carbon emissions produced by consumption of fuel which is related to the load and travel distance of special vehicles. The carbon emission cost is computed as follows:  
\[C_{3}=p\sum_{v\in V}\sum_{f\in F}\sum_{(i,j)\in A}eE\big{(}q_{ij}\big{)}d_{ij}x_{ ijv}^{f} \tag{3}\]  
where \(e\) is the emission factor of CO\({}_{2}\) and p is the unit carbon tax. \(E\big{(}q_{ij}\big{)}\) is the relationship between unit distance fuel consumption and load, which can be computed as follows [31]:  
\[E\big{(}q_{ij}\big{)}=\frac{\varepsilon_{mv}-\varepsilon_{0v}}{Q_{v}}q_{ijv}^{ f}+\varepsilon_{0v} \tag{4}\]  
where \(\varepsilon_{mv}\) is the unit distance fuel consumption with a full load of special vehicles \(v\). \(\varepsilon_{0v}\) is the unit distance fuel consumption with an empty load of special vehicles \(v\). \(Q_{v}\) is the maximum load of special vehicles \(v\). \(q_{ijv}^{f}\) is the freight quantity delivered by special vehicles \(v\) from node \(i\) to \(j\) at _f-th_ trip.
## Appendix B Pseudospectral Method  
Compared to the FD method, the Pseudospectral Method (PsM) requires fewer spatial points to give the same discretization as FDM [42], which is advantageous in solving larger3-D problems. In addition, when using the PsM, the Discrete Fourier Transform (DFT) of some variable can be calculated at every discrete time step. Disregarding the y-direction for the moment, let a signal \(r(x,t)\) (periodic in space) be given on the interval \(0\leq x\leq 2\pi\), and the space grid be formed by \(N=1024\) points with space step \(\Delta x=2\pi/N\). Now, the DFT is defined by  
\[R(k,t)=DFT(r)=\sum_{j=0}^{N-1}r(j\Delta x,t)\exp(\frac{2\pi ijk}{N}), \tag{10}\]  
and the Inverse Discrete Fourier Transform (IDFT) is  
\[r(j\Delta x,t)=IDFT(R)=\frac{1}{N}\sum_{k}R(k,t)\exp(-\frac{2\pi ijk}{N}). \tag{11}\]  
Here, \(j\) is the space grid number, \(i\) is the imaginary unit, and the wavenumbers are \(k=0,\pm 1,\pm 2,\ldots,\pm(N/2-1),-N/2\).  
The space derivatives of the function \(r(x,t)\) can be calculated as  
\[\frac{\partial r(x,t)}{\partial x}=IDFT(ikDFT(r)), \tag{12}\]  
\[\frac{\partial^{2}r(x,t)}{\partial x^{2}}=-IDFT(k^{2}DFT(r)). \tag{13}\]  
The potential maximum resolution of the PsM is affected by the number of gridpoints N in each direction. As this number rises, higher wavenumbers k can be taken into account and represented. In cases where simulation can become unstable due to various sources of instability, such as dispersion, the effect of the higher wavenumbers can be filtered out to ensure convergence, and reasonable time step size and computational time. One possible way is to apply the following filter \(f\) to the wavenumbers \(k\) before they are multiplied into Equation (12) [42]  
\[f(k,k_{1},k_{0})=\begin{cases}sin^{2}\frac{k+k_{0}-2k_{1}}{2(k_{0}-k_{1})}\pi& k_{1}<k<k_{0},\\ sin^{2}\frac{k-k_{0}+2k_{1}}{2(k_{1}-k_{0})}\pi&-k_{1}>k>-k_{0},\\ 1&-k_{1}\leq k\leq k_{0},\\ 0&k\geq k_{0},k\leq-k_{0}.\end{cases} \tag{14}\]  
Here, \(k_{1}\) and \(k_{0}\) depend on the number of available spatial points and the constitution of the problem itself, such as the desired resolution, excitation frequency, etc. If wave numbers \(|k|<k_{1}\) are passed, wave numbers \(k_{1}<|k|<k_{0}\) are partly suppressed, and the influence of wave numbers \(|k|>k_{0}\) vanishes.
# A Comprehensive Review of Acoustic Methods for Locating Underground Pipelines
## 3 Acoustic Methods for Locating Buried Pipelines  
The basic principle behind acoustic methods is that when one part of the pipe or the medium structure is excited by a specially designed source in a controlled way, waves can be produced and then travel away from the excitation point to the external medium surrounding the pipe or internal medium inside the pipe. After the interaction between these structures, wave signals will be captured by the measurement equipment located on the ground surface. Location of the object then can be determined by processing and analysing these responses, reference [12]. The block diagram of principle of acoustic methods is depicted in Figure 3.  
In addition to the traditional acoustic transmission technique, vibro-acoustic methods for locating buried utilities have been developed rapidly on the basis of wave propagation mechanism. These acoustic locating methods can be broadly divided into two categories based on access to pipe being present or not. The overview of primary acoustic localization methods is presented in Figure 4, including traditional acoustic transmission method, pipe excitation method, point vibration measurements as well as three seismic wave methods: compressional wave method, shear wave method and surface wave method. In this section, these methods are of great interest and will be reviewed in detail.