esa-sceva/satcom-chunk-collection huggingface dataset:
# Towards the Unmanned Aerial Vehicles (UAVs): A Comprehensive Review
## 6 UAV Applications Areas
### Real-Time Monitoring of Road Traffic  
Road traffic monitoring (RTM) system constitutes a domain where the integration of UAVs has captured great interest. In RTM, the complete automation of transportation sector can be achieved through UAVs [105]. It will include the automation of rescue teams, road surveyors, traffic police and field support teams. Reliable and smart UAVs can assist in the automation of these elements. UAVs have emerged as new promising tools to gather data about traffic conditions on highways. In contrast to conventional monitoring devices such as microwave sensors, surveillance videos cameras and loop detectors, cost-efficient drones can monitor huge road segments [102]. Drones can be operated by local police to get a sharp vision of road accidents or massive security crackdown on highway criminal activities such as car theft. Other applications include vehicle identification, raids on suspect vehicles, chasing armed robbers and hijackers or anyone who violates traffic rules. It can also detect vehicle over-speeding, accidents and can assist in avoiding traffic jams and mass congestions [106]. An overview of UAV services on highway is presented in Figure 11.  
UAVs can be used for road conditions monitoring such as indicating any cracks and giving an early warning to avoid possible accidents and reduce damages. Currently, road monitoring and inspection are implemented through manned vehicles and its automation level must be improved. The integration of road inspection technology and UAVs can significantly reduce road damages. Moreover, target detection algorithms can be implemented on road cracks images taken from UAVs for effective detection.
## 3 Encapsulation Protocol
### Ssh  
**Intro**  
The SSH protocol provides users with secure remote login or other network services through encryption in unsecured networks. As a result, plaintext features are made obsolete in a dense stream of information. To identify SSH tunnel traffic, statistical features and machine learning methods have become mainstream, along with the identification of passenger protocols, applications, or services.
# A Cross-Layer Approach MAC/NET with Updated-GA (MNUG-CLA)-Based Routing Protocol for VANET Network
## 3 Background
### Challenges in VANETs  
Due to the obvious unique properties of VANETs, the routing protocol is a big issue that must be overcome before these systems can be implemented. The number of packets will be sent from the source input to the output using the available vehicles. However, because of the dense population of vehicles and the tremendous nature with continuous density variations, simple traffic lights and bridges may cause a network partition, posing a major difficulty for routing. On the contrary, routing system design features, such as mobility limitations and constant road mobility, help VANETs. Extra information, such as GPS location and city maps, can indeed be made accessible. The present routing protocols suggested to VANETs could be grouped into the following classes: mobility-orientated activity algorithms, which use relative movement variables, such as radius, velocity, acceleration, and orientations, to forecast the lifespan and extent of a path from source to destination; connectivity protocols that have been proposed are used to verify the stability and confidentiality of VANET communication via infrastructure, such as RSUs and cellular ground stations; geographical location routing protocols, in which VANETs employ GPS coordinates to find paths closer to the destination vehicle, and probabilistic routing protocols, in which probability principles are used to predict incidents, including a broken link and the projected communication time [22].
# Modeling and Optimization of NO2 Stations in the Smart City of Barcelona
## 4 Methodology
### Behavior Prediction of NO\({}_{2}\)  
Afterward, the NO\({}_{2}\) concentration predictions for each station are analyzed using different methodologies. The objective is to determine which methodology best approximates the data for each particular station. Drawing on several studies [12; 48; 49], we decide to utilize the following methods:  
1. K-Nearest Neighbors (KNN): This algorithm is based on the idea that data points with similar characteristics tend to have similar output values. It works by finding the K closest points in the training dataset and predicting the output value based on the majority of the K-nearest neighbors [50].
2. Decision Tree: This model uses a decision tree to make predictions. Each internal node represents a feature, each branch represents a decision rule, and each leaf represents the prediction result [51].
3. Support Vector Regression (SVR): SVR is a regression technique based on support vectors that seeks to find an optimal regression function within a feature space. It uses a supervised learning approach to predict output values [52].
4. Random Forest: Random Forest is an ensemble of decision trees, where each tree votes for the predicted output. The final prediction is determined by selecting the output with the most votes. By combining multiple trees, the risk of overfitting is reduced, and the prediction accuracy is improved [53].
5. Artificial Neural Network (ANN): ANN is a model inspired by the structure and functioning of the human brain. It consists of a network of interconnected artificial neurons used for making predictions. The model learns from training data by adjusting the synaptic weights of neurons [54].  
All the prediction models proposed in this study were validated with the Holdout method, where a 70% data split was used for training (training) and the remaining 30% for testing (test). This division made it possible to evaluate the predictive capacity of the models [55]. By applying these methodologies to predict NO\({}_{2}\) concentrations at various stations, we aim to identify the best-suited approach for each station based on its specific characteristics and patterns. This analysis will contribute to a better understanding of the performance of different prediction techniques in capturing the complexities and variations of NO\({}_{2}\) concentrations across different locations. To compare the effectiveness of each method at the stations, we use evaluation metrics such as Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and the Coefficient of Determination (R\({}^{2}\)). These metrics help determine which models offer the best approximation to the observed data at each station. For more details about these statistics, readers are referred to [PERSON] et al. [56].
# Advances in Toughening Modification Methods for Epoxy Resins: A Comprehensive Review
## 6 Nanoparticle Toughening Method
### Hybrid Epoxy Nanocomposite
#### 6.3.2 Core-Shell Rubber/Epoxy Resin Nanocomposites  
[PERSON] et al. [108] systematically investigated the regulation mechanisms of material properties by individually and synergistically incorporating core-shell rubber (CSR) and carboxyl-functionalized carbon nanotubes (CNTs) into a benzoxazine-epoxy-phenolic (BEP) ternary thermosetting resin system. The results demonstrated that CSR enhanced the toughness by 61% through cavitation effects and matrix shear yielding, while CNTs improved the flexural strength by 8% via bridging effects and interfacial reinforcement. The synergistic combination of CSR and CNTs further achieved a 160% increase in toughness and a 30% enhancement in strength, accompanied by an elevation of the glass transition temperature (Tg) from 193.6 \({}^{\circ}\)C to 196.8 \({}^{\circ}\)C, with the thermal decomposition temperature remaining stable at 360 \({}^{\circ}\)C. Microscopic analysis confirmed that the uniform dispersion of CSR induced plastic deformation, and the carboxyl groups on CNTs catalyzed curing while strengthening interfacial bonding. The performance optimization was attributed to the synergistic cavitation-bridging-pull-out multi-mechanisms enabled by CSR and CNTs.
# Efficient Opportunistic Routing Protocol for Sensor Network in Emergency Applications
## 1 Introduction
On the other hand, isolated nodes (not part of a cluster) forward data packets opportunistically to a node among a number of nodes, based on their data delivery probability. Figures 2-4 illustrate such an opportunistic network based on the ODCR protocol, where both nodes in cluster and isolated nodes opportunistically create a data transmission path to forward data to the destination. This protocol forms clusters evaluating the Time to Return (TTR) parameter between nodes. The energy consumption of a node varies and depends on the role of each node in the cluster. We perform an experiment (simulation) on a realistic disaster area model [8] to measure the performance of the proposed opportunistic routing protocol. Simulation  
Figure 1: Opportunistic network for disaster recovery. results show that the proposed ODCR protocol in opportunistic forwarding or routing, increases the network lifetime and throughput and reduces the packet loss ratio compared to well-known clustering and forwarding protocols, such as LEACH, TEEN and LORA [16; 18; 21]. In summary, the contributions of the paper are as follows. * This paper integrates edge computing with opportunistic networking for disaster recovery;
* The sensors in the network process and aggregate data locally to reduce the amount of data transmitted;
* The sensors create density-based clusters where sensors with the most neighboring nodes become cluster heads. This approach reduces the number of data collectors and transmitters and distributes energy consumption equally over sensor nodes;
* The nodes, which are not part of any cluster (e.g., isolated nodes) opportunistically forward data to the forwarder nodes. These nodes, while moving, join to the cluster closest to them;
* If any node moves out of a cluster due to mobility, the cluster restoration process starts based on the node's property, called the minimum period of connectivity (MPC). This property ensures an energy efficient cluster restoration process compared to the cluster restoration process of existing protocols, which involve all nodes in cluster restoration at the end of each round. The rest of the paper is organized as follows. Section 2 presents the literature on existing opportunistic routing protocols. Section 3 presents the working principle of the proposed opportunistic density cluster-based routing protocol (ODCR). Section 4 evaluates the performance of the proposed ODCR protocol. Section 5 presents the network, energy and mobility models used in the experimental simulation along with experimental setup and results. Finally, Section 6 concludes the paper with suggested future research directions.
## 4 Video Compression Sub-Application Layer Protocol for WVSN
### D-Frame
#### 4.2.1 D-Frames Compression  
The operations performed to transmit the D-Frame are completely different from MPEG-2. D-frame compression and transmission method is described in the following steps:  
_Step 1_: D-Frame is generated as explained above. Each D-Frame is divided into 8\(\times\)8 blocks.  
_Step 2_: The number of zero elements of each block\({}_{\rm ij}\) (_BZP\({}_{ij}\)_) is computed by using function _F_(_Block\({}_{ij}\)_). The maximum number of _BZP_ is _64_, which is divided into \(N\) equal parts with _ZP\({}_{(k)}\)_ and _ZP\({}_{(k+1)}\)_ as boundaries. A level is assigned to each block of Figure 5.a, using Algorithm.2 presented in Figure 5.b.  
Figure 5: a) Blocked D-Frame, b) Algorithm to assign levels to the D-Frame blocks.  
Then a priority is assigned to each leveled block using following relation:  
\[\forall Block_{ij}\ \ \left({{\rm{l}}\subset=i\subset X\ /\ 8\ and\ {\rm{l}} \subset=j\subset X\ /\ 8}\right)\ gets\ P_{D(t)}\ \ \left({{\rm{l}}\subset=t\subset\subset\ N}\right)\ \mbox{iff}\ \ \ L_{(t)}\ \ \mbox{be assigned to}\ Block_{ij} \tag{1}\]  
D-Frame packets with priority level (_1_) (\(P_{D(1)}\)) are very important and have the maximum number of dissimilar pixels. They should be delivered to the base-station (Sink). Therefore, M-MPEG uses highly reliable schemes to transmit this type of packets (More details are presented in next sections).  
It is not necessary to transmit the packets with priority \(P_{D(N)}\) because these types of packets have minimum number of dissimilar pixels and their transmission does not important effect in video quality. The \(P_{D(2)}\) to \(P_{D(N-1)}\) are delivered to the Sink by using semi-reliable policies. In these policies, each source or relay node in communication transmits the packets to the next hop employing their probabilities. Probability assignment is described in Section 6.2.  
_Step 3_: In this step, run-length coding and then Huffman coding are applied to packets. Finally, packets with different priority levels are transmitted to lower layers of communication protocol stack.
# Super-Resolution Remote Imaging Using Time Encoded Remote Apertures
## 2 Time Encoded Remote Apertures (TERA)  
### Fundamentals of Imaging  
In this section, we overview the fundamental process of obtaining an image from an object and define terms to avoid confusion. Consider two objects emitting or reflecting light \(p_{1},p_{1}\) and an imaging system \(C\) with an aperture size \(D\) placed at a distance \(d\) from for the object (Figure 1a). To infer positions of object points \(p_{i}\), \(i=1,2\) within the scene, the imaging system redirects all rays from points \(p_{i}\), \(i=1,2\), such that they constructively interfere on exactly one point in the focal plane. In other words, the imaging system evaluates the length of a light ray, as encoded in its phase. In this case, the resolution \(r\) of the imaging system is determined by the Rayleigh's diffraction limit:  
\[r=\frac{1.22\lambda d}{D} \tag{1}\]  
where \(\lambda\) is the wavelength of the emitted light and determines how accurately the imaging system can determine the length of each ray, \(d\) is a distance between focal plane of the imaging system and the object, and \(D\) is the diameter of the aperture of the imaging system. Figure 1a shows an example of a diffraction pattern (Airy disk) with two targets (\(p_{i}\), \(i=1,2\)) resulting from a circular aperture. Another way of imaging is to measure the time of flight of intensity fluctuation. We call these intensity fluctuations as second-order coherence. One can use a short-pulsed laser to illuminate the object and lens-less time-of-flight detector to observe back-scattered light and reconstruct an image. The resolution of such an image is described by the Rayleigh criterion, except that the wavelength \(\lambda\) is replaced by the time resolution \(\tau\) of the time-of-flight detector. We call it a transient Rayleigh criterion.
# A Vision-Based Bolt Looseness Detection Method for a Multi-Bolt Connection
## 2 Methodology  
### DSSPI  
DSSPI, also known as shearography, is used for high-precision, nondestructive measurement of surface deformation. It has gained widespread industrial acceptance for nondestructive testing [26; 27]. Figure 1 illustrates the schematic diagram of the DSSPI system, which captures speckle fringe patterns.  
The shearing device (the Rochon prism in Figure 1) produces two beams that interfere almost in a common path. As a result, the temporal coherence of the laser is greatly relaxed, and the experimental setup is relatively simple. In general, DSSPI systems are typically employed to quantify deformations, pressures, and other parameters. However, in this paper, we endeavor to establish a direct correlation between fringe patterns and the states of bolts with the aim of simplifying the detection of multi-bolt looseness into an image classification task. By bypassing the intricate calculation process, the method becomes more concise and accessible. In the following part, we will explain how the fringe pattern is formed.  
The absolute value of the intensity of the shearing speckle fringe patterns \(I_{s}\) can be expressed as follows [25]:  
\[|I_{S}|=\left|4I_{0}\gamma\left[sin\left(\varphi_{0}+\frac{\Delta\varphi}{2} \right)sin\left(\frac{\Delta\varphi}{2}\right)\right]\right| \tag{1}\]  
where \(I_{0}\) is the sum of irradiances of both waves, and \(\Delta\varphi\) is the relative phase difference caused by deformation. The relationship between the gradient along x-direction of the out-of-plane deformation and the phase difference \(\Delta\varphi\) can be expressed as follows:  
\[\frac{\partial\omega}{\partial x}=\frac{\lambda\Delta\varphi}{4\pi\Delta x} \tag{2}\]  
where \(\frac{\partial\omega}{\partial x}\) is the derivative of the out-of-plane displacement along x-direction, \(\lambda\) is the wavelength of the laser, and \(\Delta x\) is the shear amount. According to Equations (1) and (2),  
Figure 1: The optical layout of the digital shearing speckle pattern interferometer.  
when \(\Delta\varphi=2n\pi\), \(\frac{\partial\omega}{\partial x}=\frac{\lambda n}{2\Delta x}\), \(|I_{\mathrm{s}}|=0\), the black interference fringes appear (\(n=0,\pm 1,\pm 2,\ldots\), for the fringe order).  
Figure 2 aids in visualizing the correlation among them: Figure 2a represents the surface deformation; Figure 2b illustrates \(\frac{\partial\omega}{\partial x}\); and Figure 2c displays the corresponding speckle fringe pattern. We can note the similarity between Figures 2b and 2c, which corresponds to the above analysis. As mentioned earlier, the proposed method seeks to establish a direct connection between the speckle fringe patterns and the bolt states, addressing looseness detection through image classification. However, it proves challenging for humans to succinctly summarize and generalize the differences in speckle fringe patterns across various states. Therefore, this study employs an RNN to generalize and classify these diverse speckle fringe patterns.
# Fingerprint Classification through Standard and Weighted Extreme Learning Machines
## 3 Background
### Feature Extractors  
Based on the classification given by [PERSON] and [PERSON] [43], there are three categories of fingerprint features representation: global, local, and fine-detail. However, only global feature descriptors are used for fingerprint classification because fingerprint classes are intuitively defined from global characteristics [2]. Therefore, feature-based approaches for fingerprint classification are closely related to the ridge orientations and the singular points representations. Ridge orientations are represented in an orientation map (OM), which is a representation of the local flow of the ridges. On the other hand, locations with ridge flow changes are selected as singular points, being two main types known as cores and deltas. Thus, each fingerprint class can be defined based on the distribution of its ridge orientations and singular points [2].  
The OM extraction is the first step in any feature-based fingerprint classification system. OM-based representations are obtained as a description of the local ridge flow for every block in the fingerprint. The OM of a fingerprint sample of \(U\times V\) pixels is a matrix of \(U/u\times V/v\) computed for orientation blocks of \(u\times v\). The OM matrix stores the orientation angles expressed in radians in the range of \([0,\pi)\) or \([-\pi/2,\pi/2)\). Once the OM is obtained, it is used for detecting singular points by analyzing the behavior of the ridges [44].  
[PERSON] et al. [2] present a refined taxonomy of the feature extraction methods for fingerprint classification. They classified the feature extractors into four categories: orientation image, singular points, ridge-line flow, and Gabor filter responses. Besides, the authors of [3; 5] extensively studied the performance of different feature extraction methods for fingerprint classification. Thus, in order to complement our proposed method based on ELMs, we use three global feature extractors with the best-reported results in the literature [3; 5], which have different characteristics and are described as follows:  
1. Capelli02 [22] is based on the orientation map of the fingerprint. The approach registers the core point by using the Poincare method [45]. Then, the fingerprint is represented by a vector of five positions, which is computed by applying a set of dynamic masks directly derived from each class. The feature vector also stores the orientations.
2. [PERSON] [23] improves the FingerCode feature vector [46] by including ridge-tracing information and singular points. Besides, the representation encodes the position and distance between the endpoints of the pseudo-ridge relative to the primary core point.
3. [PERSON] [24] represents the fingerprint by building a feature vector based on the relative measures among the singular points. Singular points are detected by computing complex filter responses at multiple scales [47]. Thus, the feature vector consists of the relative position, direction, and certainties of each singular point for each scale.
## 4 Results and Discussion
### Case Study: Temperature Influence on Retention  
To validate the methods explained in the previous chapters and to study the temperature influence on the retention factors, injections of ketoprofen were performed at constant CO\({}_{2}\) density (807 g L\({}^{-1}\)). Four temperatures were investigated and the back pressure of the system was adjusted accordingly. The concentration of the modifier was very low and considered as having no influence on fluid density. The experimental data and the model fitting with the MRM-Lan-kcKM method is given in Figure 7, while the estimated \(k_{0}\) values using the methods DIM-Lan-kcK, DIM-Lan-kcK' and MRM-Lan-kcKM are presented in Figure 8.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{\
\begin{tabular}{c} **Stationary** \\ **Phase** \\ \end{tabular} } & \multirow{2}{*}{**Solute**} & \multicolumn{4}{c}{**MRM-Lan-kcK**} & \multicolumn{4}{c}{**MRM-Lan-kcKM**} & \\ \cline{3-10}  & & \(k_{0}\) & \(M\) & \(K_{eq,L}\) & \(\mathbf{R}^{2}\) & \(k_{0}\) & \(M\) & \(K_{eq,L}\) & \(\mathbf{R}^{2}\) & \(k_{0}^{(\text{MRM-Lan-kcK})}\) \\ \hline \multirow{4}{*}{
\begin{tabular}{c} SIL-60 \\ Rosmarinic acid \\ Toluene \\ Toluene \\ SIL-100 \\ SIL-300 \\ Phenol \\ SIL-300 \\ Phenol \\ SIL-300 \\ Phenol \\ SIL-Aerogel \\ Nør  
The temperature dependence of adsorption in supercritical CO\({}_{2}\) has been studied in many previous works [31; 32; 33; 34; 35]. However, all of these works were done by varying temperatures at a fixed pressure. No matter which solutes or stationary phases were studied, the same conclusion was drawn: that the adsorbed amount increases with increasing temperature. This phenomenon was explained mainly by the density influence. With increasing temperature, the density and solvation power of CO\({}_{2}\) is lowered, favoring solute adsorption on the solid surface. In the current work, we eliminated this cause by fixing the density to a constant value, and we found that the \(k_{0}\) decreases with increasing temperature. When temperature increases, the vapor pressure of ketoprofen increases, and therefore, ketoprofen shows lower affinity to the stationary phase at higher temperatures.  
Figure 8: Estimated \(k_{0}\) values of ketoprofen on SIL-Aerogel at different temperatures. Method DIM-Lan-kck, DIM-Lan-kck’, and MRM-Lan-kckM were applied following the procedure explained in Sections 4.1 and 4.2.  
Figure 7: Retention factors of ketoprofen on SIL-Aerogel at different temperatures and model fittings according to MRM-Lan-kckM. SFC conditions: 191 bar for 318.15 K, 217 bar for 323.15 K, 242 bar for 328.15 K, and 268 bar for 333.15 K, flow rate 2 mL min\({}^{-1}\), modifier (methanol) fraction at 0.8–7.5 vol.%.