esa-sceva/satcom-chunk-collection huggingface dataset:
# Sample, Scalable Route to Produce Transparent Superhydrophobic/Hydrophilic Film Surfaces
## 3 Results and Discussion
### The Effects Chemical Composition on WCA and Transmittance  
To better investigate the chemical nature of the change in wettability on different silica-based coating, XPS measurements were carried out on sol-gel, sol-gel + HNT, and sol-gel + f-SiO\({}_{2}\) samples. As seen in the survey spectra in Figure 4 and the atomic composition in Table 1, the three main components are C, O, and Si. Three peaks of the XPS spectra at 99, 110, 285, and 532 eV represent signals of binding energies for Si2s, Si2p, C1s, and O1s, respectively. The %Si increases from 24% in the sol-gel sample to 30% and 38% for the sol-gel + HNT and sol-gel + SiO\({}_{2}\), respectively. It has been reported previously that Si-based components increased the hydrophobicity of the films [30; 31]. The aluminol groups on the surface of HNT could not be detected for two reasons: the sol-gel method and the depth of the analyzed area by XPS (XPS can only examine the outermost layer).  
C1s peaks of sol-gel, sol-gel + HNT, and sol-gel + SiO\({}_{2}\) with a Gaussian-curve fitting point out chemically different C species(see Figure 5). Two typical peaks are located at 284.6 and 285.7 eV, which are usually assigned to adventitious carbon, sp\({}^{2}\)-hybridized carbon, and the oxygen-containing carbonaceous bonds (C-OH) [32]. The sharp peak at positions 286 and 288 could be attributed to C = O and O = C-O [30]. However, the C1s from sol-gel + SiO\({}_{2}\) is deconvoluted into two O-Si-C-only peaks. This suggests that the surface is covered by SiO\({}_{2}\) particles. The deconvolution of Si 2p spectra shows two main components with different corresponding percentages to the Si-O-C bond (Silicon oxycarbide) at 103.0 eV and Si-O bond at 103.7 eV [33; 34; 35].  
\begin{table}
\begin{tabular}{c c c c} \hline
**Samples** & **\%O** & **\%C** & **\%Si** \\ \hline sol–gel & 49.2 & 16.5 & 24.3 \\ \hline sol–gel + HNT & 44.7 & 25.6 & 29.7 \\ \hline sol–gel + SiO\({}_{2}\) & 50.7 & 11 & 38.3 \\ \hline \end{tabular}
\end{table}
Table 1: Atomic percentage determined by XPS for sol–gel, sol–gel-HNT, and sol–gel + SiO\({}_{2}\).  
Figure 3: Coatings’ transmittance and WCA images without and with thermal treatment.  
Figure 4: X-ray photoelectron spectroscopy (XPS) survey spectrum of sol–gel, sol–gel + SiO\({}_{2}\), and sol–gel + HNT.  
Figure 5: High-resolution XPS spectra of the C1s and Si 2p core level for sol–gel, sol–gel + HNT, and sol–gel + f-SiO\({}_{2}\).
# Multiple-Intelligent Reflective Surfaces (Multi-IRSs)-Based NOMA System
## 2 The Modeled IRS-NOMA System
From (5), given that \(SE_{IRS-NOMA}^{u}\) has a predetermined target value; then the required transmission power by the IRS-NOMA system could be obtained as  
\[P_{IRS-NOMA}^{u}(N)=\left(2^{(SE_{IRS-NOMA}^{u})}-1\right)* \tag{6}\] \[\frac{z^{u}+\sum_{\
u=u+1}^{U}P_{IRS-NOMA}^{v}\left(\Gamma_{SD}^{ u}+Na\Gamma_{IRS}^{u}\right)^{2}}{\left(\Gamma_{SD}^{u}+Na\Gamma_{IRS}^{u} \right)^{2}}.\]  
On the other hand, the total power consumed by the IRS-NOMA system with \(N\) reflective elements is  
\[P_{Tot-cons}(N)=NP_{E}+P_{S}+P_{D}+\frac{P_{IRS-NOMA}^{u}(N)}{\eta_{PA}} \tag{7}\]  
where \(P_{E}\), \(P_{S}\), and \(P_{D}\) denote the dissipated power by each reflective element, by the source, and destination, respectively, whilst the term \(\eta_{PA}\) represents the efficiency of the power amplifier.
## 2 Materials and Methods
### Analytical Determinations  
Total acidity [36], organic acids [37], volatile acidity [38], pH [39], ethanol content [40], sugar content [41], and free and total SO\({}_{2}\)[42] were evaluated according to the use of the standard methods of OIV. Acetaldehyde, ethyl acetate, and higher alcohols were analyzed using a gas chromatograph system (GC-2014; Shimadzu, Kyoto, Japan) using direct injection [15]. Samples were injected into a 30 m \(\times\) 0.32 mm column with a 0.25 \(\upmu\)m film thickness (Zebron ZB-WAXPlus; Phenomenex, Torrance, CA, USA) using 1-pentanol (162 mg/L) as an internal standard. Helium served as carrier gas. A Shimadzu gas chromatograph (Japan) equipped with a flame ionization detector was used. The oven temperature ranged from 40 \({}^{\circ}\)C to 200 \({}^{\circ}\)C. The oven temperature ranged from 40 \({}^{\circ}\)C for 5 min, then 5 \({}^{\circ}\)C/min until 200 \({}^{\circ}\)C for 10 min, while the injector and detector temperatures were maintained at 220 \({}^{\circ}\)C. The main volatile compounds were analyzed using the solid-phase microextraction (HS-SPME) method. Five mL of each sample was placed in a vial containing 1 g NaCl closed with a septum-type cap. HS-SPME was carried out under magnetic stirring for 10 min at 25 \({}^{\circ}\)C. After this period, an amount of 3-octanol as the internal standard (1.6 mg/L) was added, and the solution was heated to 40 \({}^{\circ}\)C and extracted with a Divinylbenzene/Carboxen/Polydimethylsiloxane (DVB/CAR/PDMS) fiber (Sigma-Aldrich, St. Louis, MI, USA) for 30 min by insertion into the vial headspace. The compounds were desorbed by inserting the fiber into a Shimadzu gas chromatograph GC injector for 5 min. The following glass capillary column was used: 0.25 \(\upmu\)m Supelcowax 10 (length, 60 m; internal diameter, 0.32 mm). The fiber was inserted in split-splitless mode. The compounds were identified and quantified by comparisons with calibration curves for each compound.
## 3 Discussion: Two Mechanisms of THz Resonance Excitation for the Proposed Detector
### New THz Resonances for the Tunneling Effect
In this case, we can solve the MTL Equation (12) in Section 2, assuming that the source terms \(\mathbf{V}_{eq}^{\prime}\), \(\mathbf{I}_{eq}^{\prime}\) are only active on the outer line (say, Line 1), whereas they are put to zero on the other line (say, Line 2):  
\[\mathbf{V}_{eq}^{\prime}=[V_{1,eq}^{\prime}\ \ 0]^{\mathrm{T}},\quad\mathbf{I}_{eq}^ {\prime}=[I_{1,eq}^{\prime}\ \ 0]^{\mathrm{T}} \tag{29}\]  
Let us assume that the circuit is illuminated by the external field and the line is left open-circuit at both ends. The external field is chosen such to induce a current of 0.2 mA along the outer line (Line 1). In Figure 7 we plot the voltages obtained at the two ends of the GNR contacts for varying values of the frequency of impinging field, with or without the tunneling effect. The result suggests the possibility of detecting an incoming field in the THz range, thanks to the resonance effect due to tunneling. The possibility of exciting these resonances is strictly related to the unbalanced currents flowing in the two lines, as in the proposed case. The opposite case would be that of assuming that the external field induces exactly the same current on the two lines, _i.e._, a pure common mode current is excited. In this case, the self and mutual terms related to tunneling cancel themselves. This result is shown in Figure 8, where the terminal voltages are calculated in presence of a common current of 0.01 mA excited by the external field. The resonance peak appears again only if the currents are misbalanced, as shown by the results in Figure 8, referring to different values of the relative difference \(\Delta I=\left|\left(I_{1}-I_{2}\right)\right|/\left|I_{2}\right|.\)  
Figure 6: Modulation of the width of absorption peak in the transmission coefficient \(\left|\,S_{21}(\omega)\,\right|,\) due to the collision frequency.
## 2 Preliminaries
### Quadrotor's Dynamics Model  
The quadrotor's translational dynamics model based on Euler-Lagrangian are given by [22]  
\[\ddot{\eta}_{1}=-g\left[\begin{array}{c}0\\ 0\\ 1\end{array}\right]+J_{1}(\eta_{2})\left[\begin{array}{c}0\\ 0\\ u/m\end{array}\right]-\frac{k_{t}}{m}\dot{\eta}_{1} \tag{1}\]  
where \(\eta_{1}\) represents \(x\), \(y\) and \(z\) position, \(\eta_{2}\) represents yaw(\(\psi\)), pitch(\(\theta\)), roll(\(\phi\)) motion, the mass and gravity acceleration represented by m and g respectively, \(k_{t}\) represents the drag coefficient, and \(J_{1}(\eta_{2})\) is the transformation matrix linked to the Euler angles: yaw(\(\psi\)), pitch(\(\theta\)), roll(\(\phi\)), as follows  
\[J_{1}(\eta_{2})=\begin{bmatrix}c\theta c\psi&-s\psi c\phi+c\psi s\phi s\theta &s\phi s\psi+c\phi c\psi s\theta\\ c\theta s\psi&c\phi c\psi+s\psi s\phi s\theta&-s\phi c\psi+c\phi s\psi s\theta\\ -s\theta&s\phi c\theta&c\theta c\phi\end{bmatrix} \tag{2}\]Assuming \(\phi\
eq 90^{\circ}\), \(\theta\
eq 90^{\circ}\), and the property of \(J_{1}(\eta_{2})\) is  
\[J_{1}^{-1}(\eta_{2})=J_{1}^{T}(\eta_{2}) \tag{3}\]  
where \(s(.)=\sin,c(.)=\cos,t(.)=\tan\).  
The thrust force \(u\) is as follows  
\[u=f_{1}+f_{2}+f_{3}+f_{4} \tag{4}\]  
where the upward lifting forces equal to \(f_{i}=k_{i}\Omega_{i}^{2}\), where the positive constants denoted by \(k_{i}\), and \(\Omega_{i}\) represent the motors' angular speeds.  
The quadrotor's rotational equation is as follows  
\[\dot{\
u}_{2}=I^{-1}(-(\
u_{2}\times I\
u_{2})-I_{R}(\
u_{2}\times z_{e}) \Omega-k_{r}\
u_{2}+\tau) \tag{5}\]  
where \(\
u_{2}\) represents the angular velocity vector, \(I=\text{diag}(I_{x},I_{y},I_{z})\) represents the quadrotor's inertia, \(z_{e}^{T}=[0,\ 0,\ 1]\), propeller inertia denoted by \(I_{R}\), cross product denoted by \(\times\), rotational drag denoted by \(k_{r}\), and  
\[\Omega=\Omega_{1}-\Omega_{2}+\Omega_{3}-\Omega_{4} \tag{6}\]  
The torques \(\tau=[\tau_{p},\ \tau_{q},\ \tau_{r}]^{\top}\) and force \(\mathbf{u}\) on the body frame. The quadrotor's translational and rotational motion is as follows  
\[\left[\begin{array}{c}\tau\\ u\end{array}\right]=\left[\begin{array}{c}\tau_{p}\\ \tau_{q}\\ \tau_{r}\\ u\end{array}\right]=\left[\begin{array}{cccc}0&l&0&-l\\ l&0&-l&0\\ d&-d&d&-d\\ 1&1&1&1\end{array}\right]\left[\begin{array}{c}f_{1}\\ f_{2}\\ f_{3}\\ f_{4}\end{array}\right] \tag{7}\]  
where the drag factor is represented by \(d\) and the distance between the motors and center is represented by \(l\).  
The system used in the above equations is an under-actuated system, because the number of the degrees of freedom is higher than the actuation. The under-actuated systems considered in this paper are as follows  
\[\ddot{q}=f(q,\dot{q})+G(q)u \tag{8}\]  
where the coordinates vector is represented by \(q\), the dynamic vector represented by \(f\), the input matrix represented by \(G\), and \(u\) is the control inputs. Details of quadrotor dynamic are available in [22; 32].
## 3 Materials and Methods
### Geometric Correspondence
#### 3.5.2 Metrics for Measuring Similarity  
Shape matching involves the use of a similarity metric function to compute the similarity between the template picture and the image being compared [30; 31]. Matching is deemed successful when the similarity surpasses a predetermined level.  
The gradient vector of the desired edge points serves as the foundation for form matching. A point set is \(m=\langle(x_{i},y_{i})|i=1,2,3\ldots n\rangle\) created using a template outline. The variable n is the number of points in the set, and the variable \(d_{i}^{m}=\left(m_{x},m_{y}\right)^{T}\) is the gradient vector associated with each point \(m_{x}\) and \(m_{y}\).The variables represent the gradient vectors in the directions \(x\) and \(y\), respectively. The matching point of the picture to be matched is obtained based on the positional relationship between the template reference  
Figure 5: Geometric similarity assessment.  
Figure 6: Edge detection: (**a**) the initial image; (**b**) Sobel edge detection; (**c**) difference of Gaussians (DoG); (**d**) Canny edge detection.  
point and the edge point. Additionally, the gradient vector corresponding to the point \(d_{x,y}^{s}=\big{(}s_{pi}(x,y),s_{qi}(x,y)\big{)}^{T}\) is also determined. During the matching process \(w=(x,y)^{T}\), the similarity measure between the picture and the template is computed at a certain place. This computation is represented by Equation (9).  
\[S(x,y)=\frac{1}{n}\sum_{i=1}^{n}\Big{(}d_{i}^{m},d_{x+x,y+y_{i}}^{s}\Big{)}= \frac{1}{n}\sum_{i=1}^{n}s_{pi}s_{q}(x+x_{i},y+y_{i}) \tag{9}\]  
Nevertheless, the similarity metric function in Equation (9) is susceptible to changes in illumination. Therefore, it is imperative to normalize the equation, namely, through the mode-taking method for the edge gradient vectors, in order to reduce the impact of illumination.  
\[S(x,y)=\frac{1}{n}\sum_{i=1}^{n}\frac{\left(d_{i}^{m},d_{x+x_{i},y+y_{i}}^{s} \right)}{\left\|d_{i}^{m}\right\|\cdot\left\|d_{x+x_{i},y+y_{i}}^{s}\right\|} \tag{10}\]  
According to the normalized correlation function in the above equation, its computational cost can be expressed as \(O(kn)\), where k represents the number of template pixels and n represents the number of image pixels. The number of templates and the number of target image edge points determine the algorithm's efficiency. Given that the template picture is already established, we can enhance the effectiveness of template matching by decreasing the number of unnecessary edge points in the target image.
## 2 Materials and Methods
### Preparation of Ultrasonicated Aqueous Garlic Extract  
Fresh bulbs of garlic (_Allium sativum_, obtained from a local market, Gwangjin-gu, Seoul, Korea) were washed using tap water, peeled, cut into small pieces, and then dried in the oven at 55 degrees Celsius for seven days. The dried garlic was grounded using an electric blender, and 20 g of powder was measured and mixed with 100 mL of distilled water (DW) in a conical flask by the stirring machine. Four samples of 10 mL each were taken from the aqueous garlic mix, placed in a plastic tube, sonicated (Sonopuls HD 2200 probe ultrasonicator, Bandelin GmbH and Co. KG, Berlin, Germany) at 20 kHz frequency for 10 min at 100 power (W), and placed in a shaking incubator for 24 h at 300 rpm. After incubation, all sample solutions were separated from impurities by centrifugation at 10,000\(\times\)\(g\) for 5 min, the supernatants were collected, and the pellets were discarded (ultrasonicated supernatant extract). Different concentrations of 100, 80, 60, 40, 20, 10, and 5 mg/mL were prepared from the ultrasonicated extract by dilution with distilled water (DW) and then tested against all four tested bacteria. Two more samples of 40 mg/mL were prepared from the sonicated aqueous garlic mix. The ultrasonicated samples were either filtrated by using Whatman No. 1 paper filter (ultrasonicated extract without centrifugation) or centrifuged (ultrasonicated supernatant). Another sample of the same concentration was prepared without sonication (extract without sonication or unsonicated extract).
# New Quality Control Algorithm Based on GNSS Sensing Data for a Bridge Health Monitoring System
## 3 Bridge Experiment
### Improved Accuracy of GNSS Positioning Solution  
The aim of this sub-section is to demonstrate that the application of the developed algorithm for quality control increased the accuracy of the horizontal and vertical positioning.
# Pedestrian and Cyclist Detection and Intent Estimation for Autonomous Vehicles: A Survey
## 8 Datasets  
Due to its real-world application and significance, pedestrian and cyclist detection has been a widely studied problem. The key challenges that associated with this field have been variations in pose, scaling and occlusion. These effects can be seen in major datasets, such as the Caltech dataset, where several pedestrians are affected by occlusion [25]. Pedestrians and cyclists are traditionally considered separately, which can lead to having the input image scanned multiple times to detect the two objects independently. This not only increases computational costs, but it can further cause detection errors where the pedestrian and cyclists are misclassified due to the similarity in appearance.  
A dataset is used to train a deep network for pedestrian and cyclist detection. They can also be used for benchmarking and comparing the accuracy and performance of pedestrian detection techniques, as in Table 4. For deep network models, large annotated datasets are required to produce an accurate system [50]. The pedestrians, or any other object in the dataset, needs to have bounding boxes that are annotated. For general object detection, ImageNet has proven to be a sufficient dataset to train a CNN [44; 143; 144].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Miss Rate** & **Dataset** \\ \hline Shapelet [72] & 94\% & Diamler [102] \\ FrtMine (Feature Mine) [145] & 85\% & Caltech Japan [11] \\ PIs (Partial Least Squares) [146] & 72\% & Caltech Japan \\ VJ (Viola–Jones) [147] & 72\% & INRIA [32] \\ FPDW (Fastest Pedestrian Detector in the West) [148] & 63\% & TUD-Brussels [149] \\ ChnFts (Channel Features) [150] & 60\% & TUD-Brussels \\ MultiPtr+SS (Multiple Feature+Color Self Symmetry Information) [151] & 59\% & TUD-Brussels \\ LatSVM-V1 (Latent SVM) [46] & 58\% & Diamler \\ MultiPtr [152] & 57\% & Diamler \\ MultiPtr+Motion [151] & 55\% & Caltech [11] \\ HikSVM (Histogram Intersection Kernel SVM) [153] & 55\% & Diamler \\ LatSvm-V2 [35] & 51\% & ETH [154] \\ \hline \hline \end{tabular}
\end{table}
Table 4: Detection Methods Benchmark 3.  
Datasets are required for training the network to learn features for classification of pedestrians and cyclists. There exists several datasets for pedestrians, however, cyclists datasets are limited.
# Simultaneous Trajectory and Speed Planning for Autonomous Vehicles Considering Maneuver Variants
###### Abstract  
The paper presents a technique of motion planning for autonomous vehicles (AV) based on simultaneous trajectory and speed optimization. The method includes representing the trajectory by a finite element (FE), determining trajectory parameters in Frenet coordinates, composing a model of vehicle kinematics, defining optimization criteria and a cost function, forming a set of constraints, and adapting the Gaussian \(N\)-point scheme for quadrature numerical integration. The study also defines a set of minimum optimization parameters sufficient for making motion predictions with smooth functions of the trajectory and speed. For this, piecewise functions with three degrees of freedom (DOF) in FE's nodes are implemented. Therefore, the high differentiability of the trajectory and speed functions is ensured to obtain motion criteria such as linear and angular speeds, acceleration, and jerks used in the cost function and constraints. To form the AV roadway position, the Frenet coordinate system and two variable parameters are used: the reference path length and the lateral displacement perpendicular to reference line's tangent. The trajectory shape, then, depends only on the final position of the AV's mass center and the final reference's curvature. The method uses geometric, kinematic, dynamic, and physical constraints, some of which are related to hard restrictions and some to soft restrictions. The planning technique involves parallel forecasting for several variants of the AV maneuver followed by selecting the one corresponding to a specified criterion. The sequential quadratic programming (SQP) technique is used to find the optimal solution. Graphs of trajectories, speeds, accelerations, jerks, and other parameters are presented based on the simulation results. Finally, the efficiency, rapidity, and prognosis quality are evaluated.  
automous vehicles; motion planning; nonlinear optimization; integral constraints +
Footnote †: journal: Applied  
doi:10.3390/app14041579
## 4 Experimental and Results Analysis
### Discussion and Conclusions  
The experimental results demonstrate that the enhanced UniTS-SinSpec model exhibits significantly improved classification accuracy and speed compared to the baseline model and other enhanced models. Specifically, Experiment 1 indicates that the UniTS model outperforms the traditional LSTM_TSC and THF_TSC models on the selected dataset. Through an analysis of the impact of different time spans and sequence lengths on model performance, it was observed that longer time series can offer more contextual information, with one hour being identified as the most suitable, leading to a significant enhancement in classification performance. However, an excessively long sequence length may result in insufficient data or large tensors, thereby affecting the training effectiveness of the model. Experiment 2 reveals that both the sinusoidal activation function and spectral attention module notably enhance the model's performance, with the UniTS-SinSpec model demonstrating a superior performance, thus validating the efficacy of this enhanced approach. Nevertheless, it is noted that this improved model requires a longer training time, highlighting a trade-off between performance improvement and computational resources, which needs consideration in practical applications. Finally, Experiment 3 optimizes the hyperparameters for UniTS-SinSpec using Optuna, resulting in an optimized average accuracy of 97.47% for the ENF data classification task--significantly surpassing both baseline and other enhanced models. It was discovered that a lower model complexity actually contributes to an improved classification performance for ENF data; furthermore, when inputting ENF data in sequences of 5 min (300), an optimal classification performance is achieved with an average runtime of only 9.0597 s.
## 3 Results and Discussion
### Case Study in Multi-Layered Soils
#### 3.3.2 Penetration Depth and Anti-Overturning and Sliding Analysis  
Considering combined platform gravity loads, environmental loads (including dynamic inertial loads), create operational loads, and associated P-\(\Delta\) effects, the calculated preloading load was \(Q_{prc}\) = 4707.5 t. The field measurements recorded an actual penetration depth of 8.0 m at the target location. CEL displacement vector analysis revealed stratified soil responses (Figure 22):  
Figure 19: Variation curves of horizontal force and bending moment distribution on legs with axial force: (**a**) sliding resistance of LC_C1 in clay; (**b**) sliding resistance of LC_C2 in sand.  
1. At a 2-4 m penetration (LM-1 sand layer), vertical sand migration formed soil plugs beneath the spudcan, with the lateral extrusion of adjacent LM-2 clay.
2. At a 6 m penetration, complete clay displacement enabled direct contact between the LM-1 and LM-3 sand layers, triggering an abrupt resistance increase.
3. Peak resistance \(Q_{peak}\) = 8400 t occurred at a 12 m penetration, corresponding to bearing capacity failure in the LM-5 clay layer, and post-peak resistance \(Q_{post-peak}\) = 7300 t occurred at an 18 m penetration.  
Following the WTIV stability analysis framework (Figure 1), all operational scenarios (crane operation, standby, and storm survival) were evaluated:  
1. Anti-sliding stability: At a penetration depth of 8 m, with the spudcan embedded in the LM-2 clay layer, the platform's sliding resistance was calculated as \(R_{H}\) = 2407 t (Equation (5)). This exceeded the maximum horizontal environmental load of 843 t occurring during storm survival conditions.
2. Anti-overturning stability: Based on the calculation of relevant parameters in Equation (11), the most critical anti-overturning conditions for this platform occurred during the crane's operation at 90\({}^{\circ}\) outreach (\(\gamma\) = 2.437) and the storm survival condition (\(\gamma\) = 2.441).  
The platform satisfied overturning and sliding stability requirements at this site. Critical anti-overturning conditions occurred during storm survival and crane operations with significant overturning moments, necessitating a comprehensive scenario evaluation.  
The comparative penetration depth predictions using various computational methods are presented in Table 6 and Figure 23.  
Figure 21: Stratigraphic profile and geotechnical properties of target site in CEL numerical model.  
Figure 22: Surrounding soil vector diagram of typical penetration depths to reflect soil deformation, backflow, and soil-plugging effects around spudcan.