esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Collision Simulation Experiment
### Model Checking  
(1) System Energy Analysis  
The energy conversion curves of the collision process were obtained through a hypergraph, from which can be seen that at t = 0.02-0.04 s, the kinetic energy and internal energy curves change significantly at the moment of vehicle collision and the curve of total energy basically remains stable; after t = 0.08 s, the kinetic energy and internal energy curves remain horizontal, indicating that the energy conversion at the moment of vehicle collision is over. Overall, the kinetic energy of the system is converted into internal energy in the collision process, and the total energy remains the same.  
The post-processing software calculated that the percentage of the hourglass energy to the total energy of the system was 0.8%, and the mass increase was 0.99%, both of which  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Number** & **Parts** & **Material Number** & **Density (t/mm\({}^{3}\))** & **Young’s Modulus** & **Poisson’s** & **Yield Stress** \\  & & & **(Mpa)** & **Ratio** & **(Mpa)** \\ \hline
1 & Front fender & 024 & \(1.415\times 10^{-9}\) & \(1.00\times 10^{3}\) & 0.3 & 20 \\
2 & Bumper & 024 & \(7.890\times 10^{-9}\) & \(2.00\times 10^{5}\) & 0.3 & 800 \\
3 & Door and Hood & 024 & \(7.890\times 10^{-9}\) & \(2.00\times 10^{5}\) & 0.3 & 271 \\
4 & Engine & 001 & \(1.582\times 10^{-9}\) & \(2.00\times 10^{4}\) & 0.3 & \textbackslash{} \\
5 & Engine Cover & 001 & \(7.890\times 10^{-9}\) & \(2.00\times 10^{5}\) & 0.3 & \textbackslash{} \\
6 & Tailgate & 024 & \(1.005\times 10^{-9}\) & \(1.00\times 10^{3}\) & 0.3 & 20 \\
7 & Roof & 024 & \(7.890\times 10^{-9}\) & \(2.00\times 10^{5}\) & 0.3 & 220 \\
8 & Window Glass & 123 & \(2.500\times 10^{-9}\) & \(7.00\times 10^{4}\) & 0.22 & 30 \\
9 & Tire & 001 & \(1.750\times 10^{-9}\) & \(3.00\times 10^{2}\) & 0.3 & \textbackslash{} \\ \hline \hline \end{tabular}
\end{table}
Table 1: Materials and properties of major automotive components.  
Figure 1: Simulation experiment construction process.  
were far less than 5%, making the impact negligible [23]. Therefore, the finite element model is reasonable.  
(2) Vehicle Velocity Analysis  
By observing the change curve in the vehicle velocity, it could be found that the velocity of vehicle 1 decreased significantly, while that of vehicle 2 increased. After t = 0.12 s, the velocity of vehicle 1 dropped to about 6.67 \(\times\) 10\({}^{3}\) mm/s, and the velocity of vehicle 2 increased to 7.1 \(\times\) 10\({}^{3}\) mm/s, as shown in Figure 2. By comparing the velocity differences between the two vehicles, it can be inferred that the two vehicles were in a collision separation state. The change of velocity conforms to the law of real vehicle collision velocity.  
The finite element model of the whole vehicle was reasonably established through comprehensive analyses, and the collision simulation experiments also met the objective laws and relevant requirements.  
Figure 2: Change curve of vehicle velocity.
## 3 Results
### Near-Real-Time Image Query and Download Functionality  
Images with more than 10% cloud cover were discarded from the download and change detection algorithm because visual inspection showed that their radiometric quality tends to be poor due to remaining cloud fragments, haze, and cloud shadow areas. Altogether, 30 images were downloaded for the Guatem tile and 63 for the Mato Grosso tile. Where L2A surface reflectance images were available on the Copernicus Open Access Hub, they were downloaded. If only L1C top-of-atmosphere radiance image products were available, they were downloaded and atmospherically corrected with Sen2 Cor as part of the PyEO workflow.
###### Abstract
There are only few papers describing the usage of jojoba oil as a lipid-based compound in film-forming formulations, especially for fresh-cut vegetables. [PERSON] et al. [13] used different blends of jojoba oil, candellilla wax, gum arabic, and pomegranate polyphenols to improve the shelf-life quality of pears. However, [PERSON] et al. [14] showed that edible coatings based on corn starch, stearic acid, monoglycerides, and jojoba oil significantly increased the shelf life of kinnow. Jojoba oil, as a lipid obtained from the jojoba bush, is an ester of long-chain fatty acids and monovalent, long-chain alcohols, and it contains natural oxidants \(\alpha\), \(\beta\), and \(\delta\) tocopherols and, therefore, is resistant to oxidation [15]. Jojoba oil is a wax that is flowable at room temperature and has high thermal stability (up to 300 \({}^{\circ}\)C); thus, it has found a wide range of practical applications [16]. Root parsley (_Petroselinum crispum_ [Mill.] Nym. Ex A.W. Hill ssp. _Tuberosum_ [Bernh.] Mart. Crov.) is a commonly cultivated vegetable in Poland and is also an important field crop in other countries in northern Europe [17]. In general, root vegetables, including root parsley, are characterized by antioxidant, anti-inflammatory, antibacterial, and antioxidant activities, and they are often consumed as an ingredient in soups or as an additive to vegetable dishes [18]. In addition, parsley has a pharmacological effect; therefore, it is used for various medicinal purposes in folk and traditional medicine. Parsley leaves are often used as a food flavor, an antitussive, and for gastrointestinal disorders. As an anticoagulant, they are also used, among others, for skin diseases, hypertension, kidney diseases, and urinary tract infections [19]. It is also possible to isolate apigenin from parsley leaves, a bioactive compound characterized by several beneficial properties for health, but with limited therapeutic application (low water solubility and bioavailability) [20]. Treating parsley can release nutrients, as well as shorten shelf life, by enhancing bacterial growth. To prevent this, non-thermal methods are used to extend the shelf-life of products, which include, among others, high hydrostatic pressure and the use of bacteriocins or food coatings [21]. This study aimed to determine the effect of edible coatings based on whey protein isolate incorporated with jojoba oil at concentrations of 1 and 2% on selected quality characteristics, namely, hardness, color, microstructure, and the content of polyphenols and flavonoids, of fresh-cut root parsley over 28 days of refrigerated storage.
# Analysis of Weighted Factors Influencing Submarine Cable Laying Depth Using Random Forest Method
## 3 Results and Discussion  
### Factors Weight Sorted  
The dataset is divided into a training set and a test set using an 8:2 ratio. The training set is then used to train a random forest model. Based on the random forest model, importance scores are assigned to each feature to evaluate their significance. The random forest is optimized using grid search, which involves searching for the best values of three parameters: \(n_{\mathit{trex}}\) (number of trees), \(m_{\mathit{texf}}\) (minimum number of samples in a leaf node), and \(m_{\mathit{depth}}\) (maximum tree depth). After searching, the optimal parameter values are determined as follows: \(n_{\mathit{trex}}=150\), \(m_{\mathit{depth}}=10\), and \(m_{\mathit{taxf}}=5\). The resulting weights for each factor are presented in Figure 3.  
Based on the calculated importance scores and weights, it is evident that water depth and cable length have a significant impact on burial depth, collectively accounting for 55.7711% of the total importance. This result aligns with reality, since a greater water depth introduces more uncertainties, such as the influence of seabed topography, water currents, and wave action, which indirectly affect the cable through external forces. Long-distance cable laying also entails higher stress and complex environmental conditions. Therefore, considering water depth and cable length in the selection of burial depth is crucial for minimizing potential risks and losses.  
Figure 3: Weight-sorted histogram.  
Next are the geographic coordinates and deviation from the intended route, contributing 33.6108% to the overall importance. Different geographical locations exhibit varying geological conditions, water depth variations, and terrain changes. The presence of deviations may require adjustments in burial depth to accommodate the actual circumstances. Ensuring the safety and reliability of the cable along the entire construction route necessitates the consideration of geographic coordinates and route deviations as important factors.  
On the other hand, the analysis of feature importance rankings reveals that the ingress angle, in this particular dataset, does not contribute to the selected dependent variable. Its importance score is 0%. Therefore, in subsequent prediction stages, this redundant feature should be excluded.
# Stability of Halloysite, Imogolite, and Boron Nitride Nanotubes in Solvent Media
###### Abstract  
Inorganic nanotubes are attracting the interest of many scientists and researchers, due to their excellent application potential in different fields. Among them, halloysite and imogolite, two naturally-occurring aluminosilicate mineral clays, as well as boron nitride nanotubes have gained attention for their proper shapes and features. Above all, it is important to reach highly stable dispersion in water or organic media, in order to exploit the features of this kind of nanoparticles and to expand their applications. This review is focused on the structural and morphological features, performances, and ratios of inorganic nanotubes, considering the main strategies to prepare homogeneous colloidal suspensions in various solvent media as special focus and crucial point for their uses as nanomaterials.  
colloidal stability; nanoparticle dispersion; halloysite; imogolite; boron nitrides; nanotubes +
Footnote †: journal: Applied Sciences
Recent studies have explored the potential of artificial intelligence (AI) in predictive channel modeling [14; 15; 16; 17; 18; 19; 20; 21; 22; 23]. Various neural network architectures have been applied to improve path loss prediction and channel estimation accuracy. For instance, [PERSON]. [15] utilized a backpropagation neural network (BPNN) to predict path loss in aircraft cabins, achieving superior accuracy compared to the classical log-distance model. Similarly, [PERSON]. [16] employed feedforward and radial basis function neural networks (FNN and RBFNN) for predicting received power and channel characteristics in indoor millimeter-wave channels. Recurrent neural networks (RNNs) and long short-term memory (LSTM) models have been widely used for dynamic channel prediction. [PERSON]. [17] applied an RNN to predict signal amplitudes and channel impulse response (CIR) in MIMO channels, removing the need for prior channel knowledge. Building on this, [PERSON]. [18] introduced an online training LSTM model to predict channel state information (CSI) and improve performance by continuously updating the model. In high-speed scenarios, [PERSON]. [20] proposed a real-time CNN-based channel prediction model that adapted dynamically to changing conditions, outperforming traditional methods. Convolutional neural networks (CNNs) have also been combined with other techniques, such as in [19], where CNNs were integrated with 5G channel models to enhance estimation accuracy in MIMO-OFDM systems, and in [20], where CNNs were used for real-time channel prediction in high-speed environments. [PERSON]. [21] conducted a comparative study of various neural network architectures, including CNNs, RNNs, and multilayer perceptrons, for wireless channel prediction, providing valuable insights for model selection. Additionally, [PERSON]. [22] introduced a federated learning-based approach for mmWave massive MIMO systems, combining RNNs with collaborative learning, which proved effective in non-stationary environments. Finally, [PERSON]. [23] proposed a hybrid method combining graph neural networks (GNNs) with the propagation graph technique to model complex channel relationships, achieving superior results in irregular topologies. Despite these advancements, these studies highlight the limitations of single-model approaches. Challenges such as limited generalization across diverse channel conditions and reduced predictive accuracy in highly complex scenarios remain critical issues to be addressed.
# Free Vibration Analysis of Curvilinearly Tapered Axially Functionally Graded Material Beams
## 2 AFGM Beams with Variable Cross-Section
### Governing Equations of the Beams  
We consider the transverse vibrations of an AFGM beam in the \(xz\)-plane using the Timoshenko beam theory. The equations of motion governing such beams with generally variable cross-sections in terms of the transverse displacement \(w(x,t)\) of the midline and the cross-sectional rotation angle \(\varphi(x,t)\) at time \(t\) are written as follows:  
\[\begin{array}{c}-\frac{\partial}{\partial x}\Big{(}D_{11}(x)\frac{\partial \varphi}{\partial x}\Big{)}-A_{55}(x)\Big{(}\frac{\partial w}{\partial x}- \varphi\Big{)}+I_{2}(x)\frac{\partial^{2}\varphi}{\partial t^{2}}=0,\\ -\frac{\partial}{\partial x}\Big{(}A_{55}(x)\Big{(}\frac{\partial w}{\partial x }-\varphi\Big{)}\Big{)}+I_{0}(x)\frac{\partial^{2}w}{\partial t^{2}}=0.\end{array} \tag{6}\]  
The rigidities and the inertia coefficients are expressed as integrals over the cross-sectional domain in the following form:  
\[\begin{array}{c}D_{11}(x)=\int_{A(x)}z^{2}E(x)dA,\ A_{55}(x)=\kappa\int_{A( x)}G(x)dA,\\ (I_{0}(x),I_{2}(x))=\int_{A(x)}\big{(}1,z^{2}\big{)}\rho(x)dA,\end{array} \tag{7}\]  
where \(\rho(x)\) is the mass density, \(G(x)=\frac{E(x)}{2(1+\
u)}\) is the shear modulus, and \(\kappa\) is the shear correction factor. Throughout this study, the shear correction factor is accepted as \(5/6\) unless specified otherwise.  
Assuming simple harmonic oscillations, the deflection and the rotation angle can be expressed as follows:  
\[w(x,t)=\overline{w}(x)e^{i\omega t},\ \varphi(x,t)=\overline{\varphi}(x)e^{i \omega t}, \tag{8}\]  
where \(\overline{w}(x)\) and \(\overline{\varphi}(x)\) are the amplitudes of the displacement and rotation angle, respectively; \(\omega\) is the angular frequency; and \(i^{2}=-1\) represents the imaginary unit.  
Substituting (8) into (6) yields a system of coupled ordinary differential equations that describe the free transverse vibrations of a Timoshenko beam with a non-uniform cross-section as follows:  
\[\begin{array}{c}\frac{d}{dx}\Big{(}D_{11}(x)\frac{d\overline{\varphi}}{dx} \Big{)}+A_{55}(x)\Big{(}\frac{d\overline{\varphi}}{dx}-\overline{\varphi} \Big{)}+I_{2}(x)\omega^{2}\overline{\varphi}=0,\\ \frac{d}{dx}\Big{(}A_{55}(x)\Big{(}\frac{d\overline{\varphi}}{dx}-\overline{ \varphi}\Big{)}\Big{)}+I_{0}(x)\omega^{2}\overline{w}=0.\end{array} \tag{9}\]  
Here, the rigidities and inertia coefficients (7) can be conveniently presented using the following expressions:  
\[\begin{array}{c}D_{11}(x)=E(x)I(x),\ A_{55}(x)=\kappa G(x)A(x),\\ I_{0}(x)=\rho(x)A(x),\ I_{2}(x)=\rho(x)J(x),\end{array} \tag{10}\]  
where \(A(x)\) and \(J(x)\) are the area and second inertia moment of the cross-section, respectively.
## 2 Related Work
However, this is about a simulation area of 27 m \(\times\) 18 m with mobile nodes moving at 0.6 m/s and, therefore, a fairly static MANET. In [30], the impact of random and realistic mobility models on the performance of Bypass-AODV is studied. Bypass-AODV is an optimization of AODV that uses cross-layer MAC-notification. The results show that Bypass-AODV is insensitive to the random mobility models used and has a clear performance improvement compared to AODV. It has a comparable performance under a group mobility model compared to AODV. However, the study is more about a comparison between AODV and Bypass-AODV and less about how mobility models affect the performance of MANET in general. Even so, a considerable range of the speeds they used is not interesting for UAANET. Unlike the limited analysis in [30] and most of the work cited above, a general analysis framework is presented in [31]. This framework analyzes the impact of mobility on the performance of routing protocols in _ad hoc_ networks (IMPORTANT). The authors try to explain why and how mobility affects routing protocol performance. For that matter, the performance of three different routing protocols (AODV, DSR (dynamic source routing) and DSDV) is observed under four different mobility models. However, first, the mobility patterns are evaluated among themselves by means of predefined protocol-independent mobility metrics. This evaluation helps understand/explain the relative performance of the protocols across the mobility models. This work [31] is similar to what we are doing here in this paper. However, in [31], the diversity of routing protocols is limited as, for instance, AODV and DSR are both reactive protocols. Instead of having two of the three protocols being of the same general approach to routing in MANET, they could have chosen a hybrid protocol or a geographic protocol. Moreover, some of the low speed ranges that they are considering are not applicable for a study in UAANET; not to mention that we are focusing on entity mobility models mostly for searching missions applications, for instance, which is not the case in [31], where group mobility and grid patterns are considered. Each of the above reviewed related work suffers from one or more of the following shortcomings: lack of a study that targets the typical speed range of UAANET (around 55 m/s), limited diversity on the types of routing protocols, lack of comparison of the mobility models among themselves by means of protocol-independent evaluation metrics that would explain the impact on the routing protocols, non-applicability to UAANET of some of the considered mobility models, no targeted study towards entity mobility models that would more likely be used in search applications of UAANET, for example. Therefore, we feel compelled to conduct an analysis that is directed at UAANET and that resolves all of the aforementioned shortcomings. To the best of our knowledge, this is the first study/analysis of this kind.
## 4 QoS Aware Multipath Routing Protocols for WMSNs  
Routing strategy is perhaps the most studied subject in WSNs concerning communication protocols. However, routing protocols for traditional WSNs can not be directly applied in WMSNs. For one thing, in traditional WSNs routing protocols are designed to deliver scalar data over a single shortest path. Nevertheless, sending a large amount of multimedia data over the shortest path will likely cause severe network congestion and end up with early node death. Therefore using multipath routing techniques to distribute multimedia traffic over multiple concurrent paths seem to be the natural approach. On anther, in traditional WSNs the main focus of routing protocols is energy efficiency while little to no QoS provision is offered. But in WMSNs, QoS is a major concern as a matter of fact that multimedia data needs to be sent in real-time and reliably.  
In this section we present a brief survey of multipath routing protocols with QoS guarantees. A comparison of the surveyed protocols is given in Table 3. Although a few related surveys on multipath routing protocols have recently been published [119; 120; 121], however our survey studies a largely complete different set of protocols. At the same time we practice the methodology that behind every multipath routing protocol there is a classic single path routing.  
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Protocols** & **DGR** & **AntSensNet** & **Z-MHTR \& Z. [PERSON] et al. [49]** & **GEAM** & **LCMR** \\ \hline
**Routing method** & geographic routing & ant colony based routing & ZigBee cluster-tree routing & geographic routing & ad-hoc on-demand distance vector routing \\ \hline
**Routing metric** & geographic distance and deviation angle & pheromone value of residual energy, delay, packet loss rate and available memory & network address & geographic distance & end-to-end delay \\ \hline
**Routing states** & one hop neighbor table, table table table & one hop neighbor table, branes used for tree routing, and/or interfering node table & one hop neighbor table, district information & routing table \\ \hline
**Disjoint paths** & yes & yes & yes & no \\ \hline
**QoS metrics** & reliability, throughput & reliability, delay, throughput & throughput & throughput & ddelay, throughput \\ \hline
**Path repair** & yes & yes & no & yes & no \\ \hline
**Scalability** & good & good & good & good & poor \\ \hline
**Congestion control** & no & yes & no & no & no \\ \hline
**Prioritization** & no & yes & no & no & no \\ \hline
**Service differentiation** & no & yes & no & no & no \\ \hline
**Energy efficiency** & medium & medium & good & good & poor \\ \hline
**Clustered** & no & yes & yes & no & no \\ \hline
**Interference aware** & no & no & yes & yes & no \\ \hline
**Year** & 2007 & 2010 & 2012 \& 2014 & 2013 & 2017 \\ \hline \hline \end{tabular}
\end{table}
Table 3: A comparison of the surveyed multipath routing protocols.
## 2 Standard Nondestructive Testing and Evaluation Techniques
### Magnetic Particle Testing  
Another type of NDT technique is magnetic particle testing (MPT). First, the specimen is magnetized, which is followed by applying magnetic particles. The presence of defects in the specimen will result in magnetic flux leakage. Therefore, the magnetic particles will aggregate together and be attracted to the defect location [32]. A diagram to demonstrate the MPT principle is shown in Figure 4.  
In the literature, MPT stands out as an optimal choice within the aerospace industry, proving to be highly effective in identifying defects within critical components such as landing gear, engine parts, and various structural elements [3].  
[PERSON] and [PERSON] presented another application of the MPT technique [33], where microcapsules were introduced to fluorescent magnetic particles to detect cracks in pipes. The results show that this approach can be valuable for elemental analysis at specific locations, such as narrow areas, where other NDT techniques are impractical.  
In addition, researchers have used MPT to locate and size stress corrosion cracks (SCCs) in pipelines before performing incremental grinding to determine the crack's depths. Using MPT yielded precise measurements regarding the location and size of the SCCs [34].  
Figure 3: A diagram demonstrating the eddy current testing. The induced currents on the specimen are deformed due to the defect.  
[PERSON] et al. [35] also studied the sensitivity and reliability of MPT for detecting surface defects in welded materials. The technique presented in this study had a sensitivity parameter with a detection rate at 0.5 mm intervals of the crack lengths and could detect all cracks and defects beyond 2.5 mm.  
Despite its ability to finely detect cracks, a significant limitation of MPT is its incompatibility with non-magnetic materials, restricting its application scope.
## 4 Trajectory Generation
### Constraint Settings
#### 4.2.2 Safety Constraints  
To guarantee the safety constraints, we use flight corridors to illustrate safe zones for the HAUV; in these corridors, it can move safely without collision. Since most of the obstructions in the underwater environment come from the terrain of the sea bed, by constructing a KD tree _treTree_ for all known terrain information \(TE\), we can quickly figure out the shortest distances \(dist_{i}\) between the \(p_{i}\) and the obstacles' spots \(m_{i}\in TE\). By setting a corridor whose width equals \(dist_{i}\), the safety of the trajectory is ensured. However, for some spots far away from obstacles or terrain barriers, such constraints may be too loose. In this case, let \(d_{i}\) be the shortest distance between pi and its neighboring spot/spots. We define the smaller one as \(dist_{i}\) and \(d_{i}\) as the width \(W_{i}\) of the \(i_{th}\) corridor.