esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Speed Planning
#### Constraints on the Planned Speed Profile along the Path  
In summary, according to Equations (26)-(29), the constraints on the planned speeds at all nodes can be expressed as:  
\[\begin{cases}v_{min}\leqslant v_{1}\leqslant v_{mmax,1}\\ v_{min}\leqslant v_{2}\leqslant v_{mmax,2}\\ \vdots\\ v_{min}\leqslant v_{n}\leqslant v_{mmax,n}\end{cases} \tag{30}\]  
and:  
\[\begin{cases}-v_{dec1}\leqslant v_{1}-v_{0}\leqslant v_{dec1}\\ -v_{dec2}\leqslant v_{2}-v_{1}\leqslant v_{dec2}\\ \vdots\\ -v_{decn}\leqslant v_{n}-v_{n-1}\leqslant v_{decn}\end{cases}. \tag{31}\]  
By substituting Equation (19) into Equations (30) and (31), the speed-planning constraints can be rewritten as:  
\[\begin{cases}v_{min}-v_{d}\leqslant e_{1}\leqslant v_{mmax,1}-v_{d}\\ v_{min}-v_{d}\leqslant e_{2}\leqslant v_{mmax,2}-v_{d}\\ \vdots\\ v_{min}-v_{d}\leqslant e_{n}\leqslant v_{mmax,n}-v_{d}\end{cases} \tag{32}\]  
and:\[\left\{\begin{aligned} -v_{dec1}+e_{0}\leqslant& e_{1}\leqslant v_{dec1}+e_{0}\\ -v_{dec2}\leqslant& e_{2}-e_{1}\leqslant v_{dec2}\\ \vdots\\ -v_{decn}\leqslant e_{n}-e_{n-1}\leqslant v_{decn}\end{aligned} \right.. \tag{33}\]  
Equations (32) and (33) can be further rewritten as:  
\[\left\{\begin{aligned} \mathbf{b}_{L}\leqslant\mathbf{A} \mathbf{e}\leqslant\mathbf{b}_{U}\\ \mathbf{b}_{lb}\leqslant\mathbf{e}\leqslant\mathbf{b}_{ub}\end{aligned} \right., \tag{34}\]  
\[\text{and }\mathbf{A}=\left[\begin{matrix}1&0&0&\cdots&0&0\\ -1&1&0&\cdots&0&0\\ 0&-1&1&\cdots&0&0\\ \vdots&\vdots&\vdots&\ddots&\vdots&\vdots\\ 0&0&0&\cdots&1&0\\ 0&0&0&\cdots&-1&1\end{matrix}\right].\]
# A Highway Pavement Crack Identification Method Based on an Improved U-Net Model
## 5 Experiments and Analysis of Results
### Evaluation Metrics  
Since the task of this paper was mainly to binary classify each pixel point in the crack image, precision, recall, and F1-score were used as evaluation metrics. Meanwhile, this paper also involved a segmentation task and adopted mean intersection over union (mIOU), which is commonly used in segmentation tasks, as an evaluation index [43; 44; 45].  
In the crack segmentation task, TP denotes the number of pixels in which the crack class was predicted as the crack class, FP is the number of pixels in which the background class was predicted as the crack class, and FN denotes the number of pixels in which the crack class was predicted as the background class. From these parameters, the precision rate, P, and the recall rate, R, can be calculated:  
Precision = \[\frac{\text{TP}}{\text{TP}+\text{FP}}\] (13) Recall = \[\frac{\text{TP}}{\text{TP}+\text{FN}}\] (14) F1 score = \[\frac{2\times\text{precision}\times\text{Recall}}{\text{precision}+ \text{Recall}}\] (15)  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Down-Sampling Section** & **Up-Sampling Section** & **Feature Fusion** \\ \hline
128 \(\times\) 128 \(\times\) 128 & 128 \(\times\) 128 \(\times\) 128 & 128 \(\times\) 128 \(\times\) 256 \\
64 \(\times\) 64 \(\times\) 256 & 64 \(\times\) 64 \(\times\) 256 & 64 \(\times\) 64 \(\times\) 512 \\
32 \(\times\) 32 \(\times\) 512 & 32 \(\times\) 32 \(\times\) 512 & 32 \(\times\) 32 \(\times\) 1024 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Feature fusion process.  
\begin{table}
\begin{tabular}{c c c} \hline \hline Hardware environment & Nvidia RTX3080 TI, Intel Core I712700h \\ \hline Software environment & Windows 10, Tensorflow 2.12.0 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Experimental environment.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Dataset** & **Number of Categories** & **Number of Images** & **Number of Labels** \\ \hline Crack500 & 3 & 3868 & 3868 \\ CFD & 3 & 1260 & 1260 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Dataset introduction.  
The mean intersection over union is used in crack segmentation to calculate the ratio of the intersection and merge the predicted and labeled areas of each class, and finally the class average is taken. For the sake of presentation, assume that there are \(k+1\) classes of segmentation targets (\(k\) is 1 in the task of this paper), then, \(p_{ii}\) is the number of pixels belonging to class \(i\) predicted to be class \(i\), while \(p_{ij}\) is the number of pixels belonging to class \(i\) predicted to be class \(j\), and \(p_{ji}\) is the number of pixels belonging to class \(j\) predicted to be class \(i\). The specific calculation formula is shown in Equation (16):  
\[mIOU=\frac{1}{k}\sum_{i=0}^{k}\frac{p_{ij}}{\sum_{j=0}^{k}p_{ij}+\sum_{j=0}^{ k}p_{ji}-p_{ii}} \tag{16}\]
## 6 System Performance and Test Case Analysis
### Testing System Performance for Large-Scale Loads
#### 6.1.1 High-Load Performance Test  
A high-load performance test aims to determine the maximum number of accessible nodes for the test host and the average node access delay at different scales.  
Here are the relevant parameter settings for the high-load performance testing:  
* Basic configuration of test controller: Intel(R)i5-10400 6 core 16 G RAM (Intel, Santa Clara, CA, USA);
* Number of under-test nodes: 20, 50, 100, 200, 400, 800;
* Heartbeat interval: 5 s;
* BSM: 10 Hz.  
The test results are shown in Figure 9. When the number of nodes is 200, the average node access delay is approximately 25 ms, less than the BSM propagation interval of 100 ms. When the CPU utilization rate reaches 95%, and the number of nodes is 800, the average node access delay is approximately 87 ms, which still meets the requirements.  
Figure 8: Schematic diagram of the load test environment.  
Therefore, the proposed testing system can meet the requirements of C-V2X field testing and has sufficient redundancy to support the practical use of subsequent testing platforms.
## 3 Finite Element Simulation and Verification
### Finite Element Model Simulation
#### 3.1.3 Boundary Conditions  
To simulate the restraining effect of the soil container on the sand, the translational degrees of freedom in the X and Y directions of the left nodes of the front wall, the X direction of the right nodes of the sand, and the Y direction of the bottom nodes of the sand were restrained. To simulate the fixed-end constraint at the proximal end of the FAS specimen and the load imposed by the MTS, the rotational and translational degrees of freedom in the Y direction were restrained at the loading point. \(u_{\mathrm{t}}\) was applied in the X direction at the loading point.  
The FAS specimen-sand interaction was simulated using the interface element in Plaxis2D [55]. The interface element, which has the same Mohr-Coulomb constitutive model as the sand, was connected to the nearby approach slab and soil elements by five sets of nodes. The interface element was characterized by the strength reduction coefficient \(R_{\mathrm{inter}}\), which can establish a correlation between soil strength (\(\varphi_{\mathrm{s}}\), \(c_{\mathrm{s}}\)) and interface strength (\(\varphi_{\mathrm{inter}}\), \(c_{\mathrm{inter}}\)) by Formula (2) [56], where \(\varphi_{\mathrm{inter}}\) and \(c_{\mathrm{inter}}\) represent the internal friction angle and cohesion of the interface element, respectively.  
\[\tan\varphi_{\mathrm{inter}}=R_{\mathrm{inter}}\cdot\tan\varphi_{\mathrm{s}} \text{, }c_{\mathrm{inter}}=R_{\mathrm{inter}}\cdot c_{\mathrm{s}} \tag{2}\]  
Two interfaces were considered in the FEM, namely, the interface between concrete and sand with a 0.91 \(R_{\mathrm{inter}}\)[55] and the interface between the sand blocker and the FAS specimen with a very small \(R_{\mathrm{inter}}\) of 0.1 to simulate the lubricating effect of the butter. Furthermore, the gap of the interface open or closing is configured to simulate the real behavior of sand. A gap is created between the sand and the FAS when the interface is under tension, and the gap is closed by filling the sand when the interface is not under tension.  
The loading procedure in the FEM can be divided into two steps. The initial stress equilibrium is the first step. The \(K_{0}\) technique can be used to directly generate the initial effective stress because of the horizontal sand surface [55]. The equation \(K_{0}=1-\sin\varphi_{\mathrm{s}}\) is the default approach used in Plaxis2D to calculate \(K_{0}\) of typically consolidated soil [55]. Applying \(u_{\mathrm{t}}\) at the loading point of the FAS specimen is the second step.
## 4 Results  
This part will consider a comparison of the systems given in Section 3. For comparison purposes, the simplest line-holding system and state machine required are added to the provided systems. The mission is a set of points, the lines between which the drone must follow. To accomplish the mission, the drone must perform the following actions: launching from the initial position \(\rm X_{0}\), flight through the series of points at the cruise speed, deceleration and landing at the specified position. During takeoff the drone must fly to point \(\rm M_{0}\) (transition point) and hover there for \(\rm M_{p0}\) seconds. Then, it is necessary to start acceleration to point 1, holding the line \(\rm M_{0}-M_{1}\). The line is considered completed if the distance to the end point is less than \(\rm M_{r}\). After that, the drone switches to moving to the next point. The simulation uses a mission of 7 points, and the final points are the landing. At point \(\rm M_{6}\) (deceleration point), the drone switches to holding position to ensure a vertical landing and then begins to descend to point \(\rm M_{7}\). The mission is considered fully completed when the system reaches the landing point. For the mission flight, the state machine block is established. Its structure can be observed in Figure 9. It has point counter, point selector and heading target calculation subsystems. At the states machine scheme (Figure 9) the input is distance to point d2p, plane position and seven points described below. The output is point coordinates, heading command and point change events. The landing event is used to stop the simulation. To compare the performance of the developed systems, we choose a high-load operation mode. The simulation is performed for cargo delivery over a mountain as one of the most stressful yet most suitable route types for the VTOL drone. The UAV should take off, climb to the 500 m altitude, fly over a \"mountain\" at this height, descend with the turning maneuver, and then decelerate over the target at a height of 100 m and land at the specified point. The evaluation criteria are the maximum horizontal and vertical deviations from the mission and the flight time. The performance metrics will include response time to control inputs, which should not exceed 5 s, and stability, characterized by less than 5% deviation from set altitude and trajectory during windy conditions. These parameters are crucial for  
Figure 8: Adaptive velocity control system tuning: (**a**) time domain; (**b**) phase trajectory. real-world applications such as emergency medical deliveries, where rapid and precise navigation is paramount. The mission parameters, as well as the simulation parameters, are summarized in Table 3.  
It should be noted that the maximum climb and descent speeds for the PID-based system are limited to [4, \(-\)3] m/s. This results in the mission having such a large climb and descent distance for a correct comparison, and the complexity of the system does not
# Recent Advances in Research from Nanoparticle to Nano-Assembly: A Review
## 3 Stimuli Dependent Techniques
### Chemical Stimuli-Based Self-Assembly of Nanomaterials
#### 3.1.3 Bio-Macromolecule-Induced Self-Assembly
The second approach is the SA via DNA origami where the shape of the template is precisely pre-modified with short and long DNA strands having active surfaces that provide binding sites to NPs. In a typical example, thio-DNA-functionalized Au NPs of five different sizes were self-assembled on the ribbon-like DNA origami as shown in Figure 4C. However, 1D and 2D SA can be controlled by short and folded DNA strands [70]. Figure 4D,E represents the various complex structures synthesized via DNA origam technology. In addition to DNA biomolecules, bio-polymers play significant role in the SA of NPs. The utilization of bio-polymers such as proteins, peptides, nucleotides, and synthetic biopolymers has become a reliable approach in SA techniques to create well-defined nanostructures [73; 74]. Owing to their low toxicity, synergistic functionality, biocompatibility, and multiple levels of structural complexity, bio-polymers are a promising candidate to engineer self-assembled NMs for drug delivery and biosensor applications for the detection of various biomolecules. The inherent ability of biopolymers to fold into complex structures enables the precise organization of NPs through various interactions, including electrostatic forces, hydrogen bonding, hydrophobic interactions, and covalent bonding. These interactions facilitate the creation of stable, functional NP-biopolymer assemblies with tailored properties [75]. For example, hierarchical structures of graphene quantum dots were developed by integrating graphene quantum dots with biopolymers for biosensor applications. pH-responsive structural changes in bio-polymers, availability of binding sites for specific biomolecule conjugation, and enzymatic hydrolysis made the graphene quantum dot-biopolymer-integrated system a versatile biomaterial with multifunctional characteristics [76]. Therefore, it is anticipated that besides DNA functionalization on NPs, the integration of bio-polymers is also a prominent technique that can be applicable in a vast range of applications ranging from biomedicine to electronic device fabrication.
# Stability of Halloysite, Imogolite, and Boron Nitride Nanotubes in Solvent Media
## 5 Conclusions and Perspectives  
It is clear the importance of nanotubular inorganic structures for their characteristics of high surface ratios, porosity, and tunable chemistry. Halloysite, imogolite, and boron nitride nanotubes are among the prospective tubular materials for industrial applications. In particular, they are considered safe materials for living organisms. Although they present a similar morphology, their chemical structure and properties are different. Chemical composition of HNTs and INTs are very similar; the surface chemistry is dominated by hydroxyl groups that make them hydrophylic. In the contrast, native BNNTs are superhydrophobic, and cannot be dispersed in most organic solvents or in aqueous media. BNNTs show greater mechanical properties ([PERSON]'s modulus of 1.22 TPa) than HNTs and INTs ([PERSON]'s modulus of 140-390 GPa). Another interesting aspect is the pH effect on surface charge. The dielectric properties of aluminum and silicon oxides in HNTs and INTs are different. Because these nanotubes undergo ionization in aqueous media in an opposite way, they generate tubes with oppositely charged inner and outer surfaces. Information on BNNT ionization is limited; in relation to chemical structure, we can assume that BNNTs can be ionized at a low pH, but this aspect should be investigated in detail. In addition, HNTs and INTs can be easily modified using hydroxyl groups on the outer surface, while BNNTs are chemically inert enough.  
In this review, we report the main aspects of the colloidal stability of hollow-shaped nanoparticles in both aqueous and organic media, as well as the main strategies to prepare homogeneous and stable suspensions ranging from selective functionalization with charged molecules like polymers, biopolymers, and surfactants to pH dependence in water. These are crucial points for the preparation of a new class of smart hybrid nanomaterials with a wide class of applications, like drug delivery, catalysis, food packaging, environmental treatment, and cultural heritage.  
Figure 5: Fabrication of stable dispersion of the functionalized BNNTs (**a**), supernatants obtained after the water suspensions of native and PNIPAM-functionalized BNNT centrifugation (**b**), and Z-average diameters of the neat and PNIPAM-functionalized BNNTs at different temperatures (**c**). Adapted from [7].  
**Author Contributions:** Writing-Original Draft Preparation, L.L., G.C., F.P., Y.S.; Writing-Review & Editing, G.L.; Supervision, S.M.  
**Funding:** The work was financially supported by the University of Palermo.  
**Conflicts of Interest:** The authors declare that they have no conflicts of interest with this work.
# A Depth-Based Hybrid Approach for Safe Flight Corridor Generation in Memoryless Planning
## 3 Problem Formulation  
The broader problem in this paper will consider a quadrotor traveling in an obstacle-dense space from a start to a destination without access to the map of the environment. The only sensing information is real-time depth data from a stereo camera constrained by its range and field of view (FoV). We assume that the robot is provided with the real-time position of itself, the starting point, and the destination. The localization data can be obtained by either integrated Global Navigation Satellite System (GNSS) modules or other local positioning systems. We aim to design a learning-based memoryless planner capable of guiding the robot through obstacles to reach the goal. Let \(\mathcal{O}_{t}\) and \(\mathcal{G}_{t}\) are the world-frame location of the robot and the destination at the current time \(t_{s},m_{t}\) is the current depth image, and the solution in the form of a straight-line path \(\mathcal{O}_{t}\mathcal{P}_{t}\) while \(\mathcal{P}_{t}\) is the current path's endpoint. Since our memoryless planner's solution only depends on the current depth image \(m_{t}\), we drop the subscript \(t\) where convenient to simplify the notation. Then, the formal problem will be finding an optimized collision-free path \(\overrightarrow{\mathcal{OP}}\) for each of every planning sequence that allows the robot to travel safely toward the destination, given \((\mathcal{O},\mathcal{G},m)\). We propose the local planner architecture as illustrated in Figure 1. The robot's real-time location \(\mathcal{O}\) is provided by a GNSS module. The goal location \(\mathcal{G}\) is prefixed and stored as a constant in the local planner. The generated optimized path will be used as a position reference for the robot's position-tracking controller.
# Metal Carbides for Biomass Valorization
## 2 Carbide Synthesis and Characterization
### Carbothermal Hydrogen Reduction (CHR)  
Mo\({}_{2}\)C/AC and Mo\({}_{2}\)/CNTs were prepared by incipient wetness impregnation using ammonium heptamolybdate. The precursors overcame a temperature-programmed carburization (TPC) under a stream of CH\({}_{4}\)/H\({}_{2}\) at 650 \({}^{\circ}\)C [30]. [PERSON] et al. prepared Mo\({}_{2}\)C carbides supported on activated carbon (AC), carbon nanofibers and carbon nanotubes, by impregnation with ammonium molybdate or molybdic acid and carbothermal hydrogen reduction at 1000 \({}^{\circ}\)C [39]. The catalysts obtained showed different crystalline phases depending upon both the Mo precursor used and the support (Figure 2).  
High-angle annular dark-field (HAADF) scanning transmission electron microscopy (STEM) was used to characterize the structure of Mo\({}_{2}\)C/CNTs prepared by carbothermal hydrogen reduction [40].  
Figure 1: Sequential synthesis scheme for carbide-phosphide composite catalysts. Inset is a TEM micrograph with composite catalysts supported and dispersed on multi-walled carbon nanotubes (MWCNTs). Reprinted with permission from [38]. Copyright American Chemical Society, 2017.  
Figure 2: X-ray diffractograms of representative catalysts showing different crystalline phases, Reproduced from [39].  
The distribution of brighter spots indicated that the Mo\({}_{2}\)C particles (Figure 3) are uniformly dispersed on CNTs supports.  
CHR is the methodology used to prepare tungsten carbide using ammonium metatungstate as W precursor at varying carburization temperatures [41]. [PERSON] et al. demonstrated the phase composition depends on the CHR temperature [42]. A well-crystallized and phase-pure W\({}_{2}\)C was formed only at 800 \({}^{\circ}\)C, above which temperature a fraction of W\({}_{2}\)C was further carburized to WC (Figure 4).  
Although not mentioned here, other methods are also used to characterize the prepared carbides for reactions. Raman spectroscopy confirms the introduction of oxygen-containing functional groups on the CNTs' surface [35]. CO-pulsed chemisorption measurements, thermal gravimetric  
Figure 4: X-ray diffraction (XRD) patterns of 30% W\({}_{2}\)C/AC prepared at different carbothermal hydrogen reduction temperatures. Adapted with permission from [42]. Copyright WILEY-VCH Verlag GmbH and Co. KGaA, Weinheim, 2008.  
Figure 3: (**a–c**) High-angle annular dark-field (HAADF) images of three different sport, (**d**) Energy Dispersive X-ray Analysis (EDX) elemental spot-scanning profiles of the three representative spots of Mo\({}_{2}\)C/CNTs catalyst. Reproduced with permission from [40]. Copyright The Royal Society of Chemistry, 2011.  
analysis (TGA) and differential thermal analysis (DTA) were utilized to characterize nanostructured molybdenum carbide [35]. Microcalorimetry of CO adsorption was performed to determine the CO uptake and to calculate the dispersion of active sites [43].
# Single-Phase, Bidirectional, 7.7 kW Totem Pole On-Board Charging/Discharging Infrastructure
## 2 Brief Detail and Parameter Design
### Switching Frequency  
Additionally, in order to design the \(L_{inf}\), the switching frequency plays a big role. It impacts on sizing of interfacing inductor which leads to improving the power density. In other means, the higher \(f_{SW}\), the smaller \(L_{inf}\) value and size. However, the improvement in power density impacts efficiency. Therefore, to achieve optimal power density and efficiency, the switching frequency range should be decided. Hence, from (1), the switching frequency is derived as follows [47].  
\[f_{SW}=\frac{V_{g}^{2}}{4L_{inf}P_{rat}}\bigg{(}1-\frac{V_{g}|sin\omega t|}{V_ {DC-link}}\bigg{)} \tag{5}\]  
where \(\omega\) is the angular frequency of the input grid voltage and \(\omega t\) varies between 0 and \(\pi\). Therefore, from (5) it is evident that the \(f_{SW}\) has a wide range and varies with respect to the input voltage.