esa-sceva/satcom-chunk-collection huggingface dataset:
# Green Strategies for Molecularly Imprinted Polymer Development
## Green Strategies in MIP Development
### Ionic Liquids (ILs) and Deep Eutectic Solvents (DES)
#### 2.2.2 ILs as Monomers  
ILs can be used as functional monomers in MIP production. Interesting applications for the incorporation of IL monomers into polymers have been published [70; 71; 72], but the range of possible structures with ILs is so broad that it still has a long way to be explored.  
To illustrate the potentiality of using ILs as monomers, [PERSON] et al. [71] described the use of 1-(\(\alpha\)-methyl acrylate)-3-methylimidazolium bromide (1-MA-3 MI-Br) IL as a functional monomer, for MIP production for caffeine. The performance of this system was compared with MAA-MIPs, in which the first one showed a superior adsorption capacity. Furthermore, the adsorption capacity of caffeine using the IL-MIPs in water showed the highest value, when compared with methanol, ACN, and methylene dichloride. So, this is a very promising result, which demonstrates IL's potential as monomer, and in addition, 1-MA-3 MI-Br-MIPs demonstrated a good compatibility with aqueous environments.  
Figure 2: Protocol for the synthesis of MIPs for l-phenylalanine using 1-butyl-3-methylimidazolium \(\alpha\)-aminohydrocinnamic acid salt [BMIM][Phe] as template. Reproduced from reference [67] with permission from John Wiley and Sons.  
Another example found in the literature is the preparation of IL-MIPs by surface imprinting. [PERSON] et al. reported the production of acrylamide modified multi-walled carbon nanotubes (MWCNTs-AAm)-MIPs (MWCNTs@BSA-MIPILs) [72] with affinity to BSA, using four different allyl-functionalized ILs as monomers. The selective recognition ability provided by the IL-based MIPs was higher for BSA over analogue molecules such as HSA, Lys, Try, and BHb. Once again, the results showed that ILs were water-compatible, which is an important parameter in biomacromolecules' stabilization.
## 5 Analysis of Prominent Clustering Routing Protocols in WSNs
### Data-Transmission Based Clustering Routing Protocols
#### 5.2.2 Teen  
Threshold sensitive Energy Efficient sensor Network protocol (TEEN) [27], proposed by [PERSON] and [PERSON], is a hierarchical protocol whose main goal is to cope with sudden changes in the sensed attributes such as temperature. The protocol combines the hierarchical technique in line with a data-centric approach. The nodes sense their environment continuously, but the energy consumption in this algorithm can potentially be much less than that in the proactive network, because data transmission is done less frequently.  
In TEEN, a 2-tier clustering topology is built as illustrated in Figure 9 and two thresholds, hard threshold and soft threshold, are defined. The former threshold is a threshold value for the sensed attribute. It is the absolute value of the attribute beyond which, the node sensing this value must switch on its transmitter and report to its CH. The latter threshold is a small change in the value of the sensed attribute which triggers the node to switch on its transmitter and transmit.  
Figure 9: Illumination of the 2-tier Clustering Topology in TEEN.  
In TEEN, a CH sends its members a hard threshold and a soft threshold. Thus the hard threshold tries to reduce data communications by allowing the nodes to transmit only when the sensed attribute is in the range of interest. The soft threshold further reduces data communications might have otherwise occurred when there is little or no change in the sensed attribute. At the expense of increased energy consumption, a smaller value of the soft threshold generates more accurate information of the network, thus users can control the trade-off between energy efficiency and data accuracy by the parameters adjustment. Moreover, the soft threshold can be varied and the users can change the fresh parameters as required at every cluster change time.  
According to above discussion, TEEN has the following advantages: (1) Based on the two thresholds, data transmission can be controlled commendably, _i.e._, only the sensitive data we demand can be transmitted, so that it reduces the energy transmission consumption and improves the effectiveness and usefulness of the receiving data; (2) TEEN is complement for reacting to large changes in the sensed attributes, which is suitable for reactive scenes and time critical applications.  
However, there exist a few drawbacks in TEEN as follows: (1) It is not suitable for periodic reports applications since the user may not get any data at all if the values of the attributes may not reach the threshold [72]; (2) There exist wasted time-slots and a possibility that the BS may not be able to distinguish dead nodes from alive ones, because only when the data arrive at the hard threshold and has a variant higher than the soft threshold did the sensors report the data to the BS; (3) If CHs are not in the communication range of each other the data may be lost, because information propagation is accomplished only by CHs [73].
# A Segmentation-Based Optimal Seamline Generation Method for SAR Image Mosaic
## 2 Materials and Methods
### Preprocessing and Seamline Network Generation  
The fundamental task of mosaicking, image registration, involves aligning images acquired at different times and under diverse imaging conditions. In this paper, a geometry-aware image registration method [33] is employed. This method extracts intrinsic orientation features and prioritizes geometry-invariant areas, ensuring consistent geometry alignment among multiple SAR images slated for mosaicking. Following the registration step, a radiometric normalization method [34] rooted in the radiometric principles of SAR is applied to eliminate the radiometric differences between images.  
Once the ortho-rectification of all SAR images is completed, the generation of the initial seamline network is executed using the AVDO method.
## 4 The Proposed HABC
The HABC algorithm utilizes the onlooker bees to search around the employed bee's neighborhood, and they also move towards the population's best solution. This movement promotes diverse solutions and explores different regions of the search space. Essentially, this transfer of information from the best solution to other solutions increases the algorithm's convergence speed. To achieve this objective, the onlooker bee is designed to make a small movement towards the best solution of the population. This movement is mathematically represented as Equation (11). \[VG_{ij}=V_{ij}+\gamma\left(XG_{ij}-V_{ij}\right) \tag{11}\]  
where \(VG_{ij}\) = movement towards the global best, \(XG_{ij}\) = the best global experience of bees, \(\gamma\) = calibration rate, and \(V_{ij}\) = onlooker bee. The process of migration happens when a guest habitat sends its species to a host habitat. In the proposed HABC, the generalized sinusoidal migration model is utilized. The emigration and immigration rates are defined in Equations (12) and (13), respectively, as functions of the number of their species. \[\mu_{k(j)}=\frac{E}{2}\times\left(-cos\left(\frac{k(j)\pi}{N}+\varphi\right)+1\right) \tag{12}\]  
\[\lambda_{k(j)}=\frac{I}{2}\times\left(cos\left(\frac{k(j)\pi}{N}+\varphi\right) +1\right) \tag{13}\]  
where \(k(j)\) represents the rank of the species living in the \(j\)-th habitat, \(N\) represents population size, and \(E\) and \(I\) display the highest rates of emigration and immigration, respectively. Figure 2 shows the flowchart of the proposed HABC. Figure 2: The flowchart of the proposed HABC. As can be seen, the proposed HABC aims to find a balance between exploration and exploitation by using both local and global search methods. The local search is performed by employed and onlooker bees, as well as a migration model, while scouts, onlookers, and movement towards the global best are used for global search. Figure 3 indicates the proposed migration model, movement towards the global best, and neighborhood search of the HABC algorithm. The fitness function is equivalent to the mean square error (MSE), and a lower MSE corresponds to a better solution.
## 1 Introduction
[19] reported that SMAT had a deleterious influence on the corrosion resistance of 304 SS in 0.6 M NaCl. [PERSON] et al. [20] studied the effect of SMAT on the corrosion resistance of 316L SS in artificial seawater and found that SMAT increased corrosion resistance during short-term tests. However, after prolonged exposure to the corrosive environment, the SMAT specimen showed a deterioration of its corrosion properties due to the accumulation of micro-stress and the presence of defects in the surface layer. [PERSON] et al. [21] investigated the influence of time and ball size on the corrosion resistance of SMAT AISI 409 SS specimens. They established that some combinations of the process parameters decreased the corrosion resistance due to the increase in micro-strain and surface defect density induced during the treatment. [PERSON] et al. [22] investigated the relationship between the nanostructure and the corrosion properties of SMAT 316L SS in 0.05 M H\({}_{2}\)SO\({}_{4}\) + 0.25 M Na\({}_{2}\)SO\({}_{4}\) aqueous solution. They identified an improvement of the corrosion resistance owing to the appearance of twin boundaries and a reduction in corrosion resistance attributed to the presence of nano-crystalline boundaries. There is relatively little research regarding the effect of static SCW on the corrosion resistance of SS. [PERSON] et al. [23] evaluated the corrosion behaviour of X6 CrNiMoTi17-12-2 SS processed by DB using a potentiodynamic method. They found that DB improves the corrosion resistance of the SS because it can provide the machined surface with a favourable geometrical structure and a suitable SI in general. [PERSON] and [PERSON] [24] found that DB improved the stress corrosion cracking resistance of duplex SS, and this improvement depends on the SCW magnitude. Based on this brief literary survey, the following conclusions can be drawn: (1) the degree of SCW is essential for both the fatigue strength and the corrosion resistance of chromium-nickel austenitic SSs, and the influence on both is not one-way; (2) DB, alone and in combination with heat treatment, is an effective and relatively inexpensive method for achieving SPD in these steels. Thus, this study aims to evaluate the effect of varying degrees of SCW in the DB process, including heat treatment, on the rotating fatigue strength and corrosion resistance of AISI 304 chromium-nickel austenitic SS.
## 3 Application Domains
### Supply Chain Management  
Embedded sensor data will be used not only inside industries during manufacturing processes but also outside of them, in order to improve supply chain performance. Combining the RFID technology with ubiquitous Internet access, _real-time monitoring of location and condition_ of products during their transit will be made possible [34]. Accordingly, _logistic tracking_ will be made easier and less prone to mistakes [35]. Data about products, destinations, drivers and warehouse requirements will be analysed and processed along with data about traffic and weather conditions for optimal _route planning_[36]. Sensors will be also used to perform real-time _quality checking_[37], i.e., to monitor the condition of products, their packaging, and their location. This will be particularly useful to ensure that products with particular requirements, e.g., temperature for frozen food, are conveyed in the best possible conditions.
## 5 Numerical Simulations and Discussion
### Experiment 4. MSE Curves of Proposed Algorithms vs. Dispersive Parameter  
Dispersive distribution of \(\alpha\)-stable noise has harmful effects. This experiment evaluates the MSE performance of proposed SLMS algorithms in different dispersive parameters \(\gamma\in\{0.5,\,1.0,\,1.5,\,2.0,\,2.5\}\) in the scenarios of \(\alpha=1.2\), \(K=8\), and SNR = 10 dB, as shown in Figure 5. Larger \(\gamma\) means more serious dispersion of \(\alpha\)-stable noise and a worse performance for the proposed  
Figure 4: Monte Carlo based MSE curves averaging over 1000 runs with respect to different characteristic exponents \(\alpha\in\{0.8,\,1.0,\,1.2,\,1.4,\,1.6,\,1.8,\,2.0\}\), in scenarios of dispersion parameter \(\gamma=1.0\), channel length \(N=128\), channel sparsity \(K=8\), and \(SNR=10\) dB.  
algorithm and vice versa. Figure 5 implies that the proposed SLMS algorithms are deteriorated by \(\gamma\) rather than \(\alpha\). The main reason is that the proposed SLMS algorithm can mitigate the amplitude effect of the \(\alpha\)-stable noise due to the fact that the sign function is utilized in the proposed SLMS algorithms.
## 2 Multi-Objective Message Routing in EnFVs
### Traditional Routing Algorithms and Their Limitations  
Traditional routing algorithms, such as shortest path algorithms (e.g., [PERSON]'s algorithm) and heuristic-based algorithms (e.g., A* algorithm), have been widely used in transportation systems to find efficient routes based on distance or time [21]. However, these algorithms have limitations when it comes to addressing the specific requirements and complexities of message routing in EnFVs. One major limitation of traditional routing algorithms is their inability to consider the unique characteristics of EVs [22]. EVs have limited range and require access to charging infrastructure to ensure uninterrupted operation. Traditional algorithms do not take into account the availability and proximity of charging stations when calculating routes. Consequently, they may inadvertently direct EVs on routes that exceed their battery capacity or fail to consider the optimal charging locations along the route. In the case of FVs, traditional routing algorithms face similar limitations. FVs, such as drones or UAVs, are subject to flight restrictions, airspace regulations, and safety constraints. Traditional algorithms do not incorporate these factors into their routing decisions, potentially leading to routes that violate airspace regulations or pose safety risks [9]. Moreover, traditional routing algorithms typically optimize for a single objective, such as minimizing the distance or travel time. In the context of EnFVs, multiple objectives need to be considered simultaneously. For example, routing decisions should not only focus on minimizing energy consumption but also on factors such as traffic congestion, vehicle load balancing, and environmental impact. Traditional algorithms lack the capability to handle multi-objective optimization, leading to suboptimal or impractical routing solutions.
## 3 Security Issues Facing CVs and Solutions
### Vanet
#### 3.4.1 Efficient Authentication with Privacy Preservation and Key Management in Group Signature  
Authentication is the primary step to ensure security in VANET, and much existing work proposed different techniques to provide authentication in VANET [62; 63; 29; 64]. Usually, the symmetric key method is significantly faster than the digital signatures; however, the symmetric key method cannot provide non-repudiation [65]. Unsymmetrical keys can provide authentication and non-repudiation, but they may cause computation complexity and communication overhead [66], as well as privacy exposure [67]. Therefore, efficient unsymmetrical authentication with privacy preservation is needed. In recent years, group signature has been applied in VAENT to protect a vehicle's anonymity besides authentic  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Services** & **Transmission Mode** & **Advantages** & & **Open Issues** \\ \hline C-V2X & V2V, V2P, V2L, V2N & (1) & Network-based communication. & (1) & Large coverage and support for high mobility. & (1) & Direct secure communication without a priori configuration of keys by the network is needed. \\ C-V2X & V2V, V2P, V2L, V2N & (2) & Direct-communication. & (2) & High market penetration. & (2) & Integrity protection on application layer messages. \\ C-V2X & V2V, V2P, V2L, V2N & (3) & High capacity and bit rate. & (3) & Privacy protection of location. \\ \hline \hline \end{tabular}
\end{table}
Table 4: Features of V2X communications and potential issues.  
cation [68; 69]. The main limitation of this approach is the computation complexity and communication overhead to distribute group keys [69; 70]. In addition, a manager is needed to distribute the key to a new member when it joins the group or revoke the key when the member leaves. Invalid and malicious signers should be identified in time, while forward privacy should be protected at the same time [71; 72]. Therefore, how to manage the key in a group signature is still challenging.
## 2 Materials and Methods
### Hyperspectral Image Transformation and Spectral Data Extraction  
The captured images were analyzed using software for hyperspectral image analysis (Environment for Visualizing Images, ENVI, Irving, TX, USA). To eliminate the effects of uneven illumination and dark current, black-and-white calibration was conducted on raw images using Equation (1):  
\[R=\frac{Ir-Iw}{Iw-Id} \tag{1}\]  
where \(R\) is the corrected reflectance image; \(Ir\) is the raw hyperspectral image; \(Iw\) and \(Id\) represent the white reference image and the dark reference image, respectively [44]. The dark reference image with 0% reflectance was collected when the lights were turned off and the lens was covered with a black cover. Accordingly, the white reference image was acquired from a white diffuse reflectance board with 99% reflectivity (Spectralon, Labsphere Inc., North Sutton, NH, USA). In the next step, a thresholding method was applied to the corrected images for the removal of the background and extraction of the region of interest (the region corresponding to the flat leaf). The mean spectra of each image were extracted from the leaf samples, by averaging the spectra of all the pixels in the corresponding region of interest.
## 6 Discussion
### Limitations  
Through qualitative and quantitative analysis of the experimental results, it was verified in this paper that the proposed method is effective and robust for the autonomous landing of the FPLIR on power lines. However, the method still has some limitations, mainly regarding the following two aspects.  
1. The accurate estimation of the relative position of the FPLIR and the power line is crucial for the FPLIR to be able to land on the power line autonomously. Since the diameter of the power line is small, the binocular camera is not able to measure the depth when the FPLIR is far away from the power line. To accurately estimate the relative position, the FPLIR and the power line need a minor initial position, which would result in a shorter attitude adjustment time during the FPLIR landing line, increasing the risk of failure.
2. MPC depends on a well-described system model to optimize system performance and ensure constraint satisfaction. Therefore, an accurate dynamics model is critical to the success of the control system. Since the FPLIR is not a standard multi-rotor UAV, there are inaccuracies in the dynamics model's description of the FPLIR. If the speed of the FPLIR is too high, it will result in poor trajectory tracking.
# WiFi-Based Gesture Recognition for Vehicular Infotainment System--An Integrated Approach
## 4 Methodology
### Gesture Detection
#### 4.2.1 Dimensionality Reduction  
In CSI data, some subcarriers are very sensitive to noise but non-significant for gesture sensitivity. From Figure 3, it is clear that the sensitivity of different subcarriers varies for the same gesture. Therefore, we suggest Principal Component Analysis (PCA) to reduce the dimensionality and eliminate such types of unpredictable subcarriers.  
PCA is commonly used to extract most representative components and removes background noise [49]. In the PCA scheme, new variables are being generated by the linear combination of the original data, called principal components. These principal components organize the original variables in orthogonal form in order to eliminate redundant information.  
Figure 3: Variance of different subcarriers for the same gesture.  
In our experiment, PCA is applied to each packet received to extract \(p\) principal components. Based on our observations, we particularly select the second, third and fourth principal components that is, \(p=3\) for both the amplitude and phase of the CSI signal. Finally, we obtain a matrix with \(p\times N_{a}\) dimensions, where \(N_{a}\) denotes the number of anomalous packets.  
First, we normalized CSI matrix \(H\) and static components were removed. Let \(H_{n}\) represent the normalized matrix after the subtraction of the average CSI value from each column of CSI matrix \(H\). We then calculated the corresponding correlation matrix as \(H_{n}\times H_{n}^{T}\). After eigen decomposition of the correlation matrix, we compute eigen vectors \(q=q_{1},q_{2},\ldots,q_{i}\) and simultaneously \(p=p_{1},p_{2},\ldots,p_{i}\) principal components are structured. For \(i\)th packet, it can be expressed as:  
\[p_{i}=H_{n}\times q_{i}, \tag{12}\]  
where \(q_{i}\) and \(p_{i}\) stand for \(i\)th eigen vector and \(i\)th principal component, respectively.
## 4 Proposed Architecture and Implementation
### Communication Proof of Concept
```
1:Input: vehicle position \(v_{cur}\), list of RSUs \(\{RSU\}\), RSU range \(r_{range}\)
2:Output: List of RSUs within range \(\{RSU_{in\_range}\}\)
3:Initialize: \(RSU_{in\_range}\leftarrow\emptyset\)
4:functionDETERMINE_RSUs(\(v_{cur},RSU_{list},r_{range}\))
5:for\(RSU\) in \(RSU_{list}\)do
6:\(dist\leftarrow\)CALCULATE_DISTANCE(\(v_{cur},RSU.position\))
7:if\(dist\leq r_{range}\)then
8:\(RSU_{in\_range}\)-add(\(RSU\))
9:endif
10:endfor
11:return\(RSU_{in\_range}\)
12:endfunction
13:functionCALCULATE_DISTANCE(\(v_{cur},RSU_{pos}\))
14:\(lat_{v},lon_{v}\gets v_{cur}\)
15:\(lat_{rsu},lon_{rsu}\gets RSU.pos\)
16:\(dLat\gets radians(lat_{rsu}-lat_{v})\)
17:\(dIon\gets radians(lon_{rsu}-lon_{v})\)
18:\(a\leftarrow\sin^{2}(\frac{dLat}{2})+\cos(radians(lat_{v}))\times\cos(radians( lat_{rsu}))\times\sin^{2}(\frac{dIon}{2})\)
19:\(c\gets 2\times\text{atan2}(\sqrt{a},\sqrt{1-a})\)
20:\(distance\gets R_{earth}\times c\)
21:returndistance
22:endfunction
```  
**Algorithm 2** Algorithm to determine RSUs in range