esa-sceva/satcom-chunk-collection huggingface dataset:
# An ECC-Based Anonymous and Fast Handover Authentication Protocol for Internet of Vehicles
## 3 Preliminaries
### Threat Model  
In vehicular network systems, the wireless channel serves as a public medium, rendering Vehicle-to-Infrastructure (V2I) communications susceptible to numerous security threats, including eavesdropping and privacy infringement. Attackers may attempt to eavesdrop on, intercept, analyze, or modify messages transmitted over the channel. The threat model is a formalization of an adversary, under which the adversary's capabilities and intentions can be programmatically characterized. The attacker's objective is to gain unauthorized access to the system and subsequently attempt to derive profit from it. By integrating the widely adopted Canetti-Krawczyk adversary model [26] and the Dolev-Yao adversary model [27], we define the attacker's capabilities as follows:  
1. Adversaries possess the capability to eavesdrop on, modify, intercept, and replay all messages transmitted over a public communication channel. However, secure transmission mechanisms are designed to prevent adversaries from intercepting such information.
2. Attackers may exploit intercepted sensitive parameters like vehicle control data, cryptographic keys and public information to initiate spoofing attacks or inject malicious messages.  
Figure 2: System model.  
The attacker may exploit session keys or the session state within a specific session to compromise confidential credentials. However, if the current session key has been compromised in a particular session, the attacker remains incapable of deducing both past and future session keys.
* Adversaries cannot extract any information from the trusted computation tree.
# Synthetic Routes to Silsesquioxane-Based Systems as Photoactive Materials and Their Precursors
## 1 Introduction
### Sonogashira Coupling for the Introduction of Silylalkynyl Moieties onto the Silsesquioxane Core  
Sonogashira coupling (also known as Sonogashira-Hagihara) is a palladium-mediated reaction between an unsaturated -C\(\equiv\)CH terminal alkyne moiety and an aryl halide (usually -Br, -I, -OTf) that results in the formation of substituted alkynes. It is conducted in the presence of copper halide and requires stoichiometric amount as a base. The variations of this process are numerous and concern catalytic systems (including additional ligands), but it is a useful process in organic and organosilicon chemistry, and tolerant of a number of functional groups [64].  
This type of coupling requires the presence of -C\(\equiv\)CH or -phenyl-halogen moiety attached to POSS core. The first reports on this process in silsesquioxane chemistry concerned using octa(bromophenyl)POSS and octa(iodophenyl)POSS derivatives with phenylacetylnes in [Pd\({}_{2}\)(dba)\({}_{3}\)] and [Pd(PtBu\({}_{3}\))\({}_{2}\)] catalyzed reaction, conducted at RT and 60 \({}^{\circ}\)C for 48 or 24 h, respectively and resulting in 56%-90% yield for -Br product, and 67%-90% yield for -I derivative [65]. In 2016, [PERSON] et al. applied this procedure using octa(iodophenyl)POSS with \(\pi\)-conjugated phenylacetylenes (Figure 15) [66]. These compounds however, were obtained with rather moderate yields (i.e., Ph3 POSS = 20%; tBuPOSS = 19%; and iPrPOSS = 22%).  
Nonetheless, all of these systems were verified in terms of their thermal resistance which was proved to be high (\(T_{\text{d}}\) = 496-526 \({}^{\circ}\)C). Their photophysical properties were disclosed via UV-vis and photoluminescence analysis and compared with their organic, trimthylsilyl analogues. The absorption spectra revealed the presence of two maxima wavelengths in the area \(\lambda_{\text{ab}}\) = 323-327 and 347-350 nm, and did not present any significant changes regarding the structural differences of substituents. The emission spectra were recorded in solution (CHCl\({}_{3}\)) and in a solid state. The relatively broader and red-shifted emission was recorded for Ph3 POSS along with higher quantum efficiency in comparison to the organic analogue. This was explained by the inorganic core presence as well as the mobility and \(\pi\)-conjugation of phenyl rings (undisturbed for alkyl derivatives). On the other hand, a strong, red-shifted \(\lambda_{\text{em}}\) maxima at ca. 405-427 nm was recorded, resulting from unfavorable intermolecular interactions. The authors reported high thermal resistance for the luminescence properties (after UV irradiation \(\lambda\) = 365 nm) that were maintained even at 250 \({}^{\circ}\)C.
# Sending-or-Not-Sending Twin-Field Quantum Key Distribution with Light Source Monitoring
## 2 SNS Protocol with LSM
### Parameters Estimation with LSM  
The source structure in the SNS protocol is similar to that of the BB84 and MDI protocol, therefore it is possible to apply the LSM scheme proposed in the BB84 and MDI protocol to the SNS protocol to estimate \(P_{n}(\mu)\). Recently, a new LSM scheme proposed by us in the MDI protocol gives tight bounds of \(P_{n}(\mu)\) in each of [PERSON]'s and [PERSON]'s part under UPC [38]. Since the SNS protocol and the MDI protocol have the same structure in the intensity modulation part, the same LSM module can be added to the SNS protocol as shown in Figure 1, and with the extra LSM module, the same results can be obtained in the SNS protocol as follows [38]:  
\[P_{0}^{\text{II}}(\mu)=P_{0}^{\text{L}}(\mu)=\frac{P^{\mu}(\eta_{0})}{1-Y_{0}}, \tag{17}\]  
\[P_{1}^{\text{L}}(\mu)=\frac{(1-\eta_{2})^{2}P^{\mu}(\eta_{1})-(1-\eta_{1})^{2 }P^{\mu}(\eta_{2})}{(1-\gamma_{0})(1-\eta_{1})(1-\eta_{2})(\eta_{1}-\eta_{2})} -(\frac{1}{1-\eta_{1}}+\frac{1}{1-\eta_{2}})P_{0}^{\text{II}}(\mu), \tag{18}\]  
\[P_{1}^{\text{II}}(\mu)=\frac{(1-\eta_{2})(1-\eta_{1})}{[1-\eta_{2}-(1-\eta_{1} )(2-\eta_{1})]}\big{\{}\frac{P^{\mu}(\eta_{1})}{(1-\eta_{1})^{2}(1-\gamma_{0} )}+\frac{[1-(1-\eta_{2})^{3}]}{\eta_{2}(1-\eta_{2})^{2}}P_{0}^{\text{II}}(\mu)\]  
\[+\frac{1-\eta_{2}}{\eta_{2}}-\frac{P^{\mu}(\eta_{2})}{p_{2}(1-\eta_{2})^{2}(1- \gamma_{0})}-\frac{P_{0}^{\text{L}}(\mu)}{(1-\eta_{1})^{2}}\big{\}}, \tag{19}\]  
\[P_{2}^{\text{L}}(\mu)=\frac{P^{\mu}(\eta_{2})}{(1-Y_{0})(1-\eta_{2})^{2}\eta_ {2}}-\frac{1-\eta_{2}}{\eta_{2}}-\frac{[1-(1-\eta_{2})^{3}]}{(1-\eta_{2})^{2} \eta_{2}}P_{0}^{\text{II}}(\mu)-\frac{2-\eta_{2}}{1-\eta_{2}}P_{1}^{\text{II}} (\mu), \tag{20}\]  
\[P_{2}^{\text{II}}(\mu)=\frac{P^{\mu}(\eta_{2})}{(1-Y_{0})(1-\eta_{2})^{2}}- \frac{P_{0}^{\text{II}}(\mu)}{(1-\eta_{2})^{2}}-\frac{P_{1}^{\text{L}}(\mu)} {1-\eta_{2}} \tag{21}\]  
with a condition  
\[\eta_{1}(2-\eta_{2})>1, \tag{22}\]  
where \(\eta_{k}(k=0,1,2)\) is the variable attenuation coefficient in the extra LSM module, \(P^{\mu}(\eta_{k})\) is the probabilities of the single photon detector's (SPD) not responding in the LSM module, and \(Y_{0}\) is the dark count rate of the SPD. When \(P_{n}(\mu)\) are estimated with the LSM scheme, the key parameters \(s_{1}^{\text{L}},e_{1}^{\text{II}}\) in Equations (14) and (15) can be calculated, and the secret key rate of SNS protocol under UPC is obtained.
## 4 Plasmon Dispersion
### Double-Layer Graphene  
Consider the double-layer graphene shown in Figure 3d with the bottom graphene layer serving as a substrate having the conductivity \(\sigma_{\mathrm{sub}}\) and \(\varepsilon_{\mathrm{l}}=\varepsilon_{\mathrm{0}}\). By applying the limit \(d_{\mathrm{sub}}\to 0\) and substituting \(\varepsilon_{\mathrm{sub}}\) in Equation (15) with  
\[\varepsilon_{\mathrm{sub}}=\mathrm{i}\frac{\sigma_{\mathrm{sub}}}{\omega d_{ \mathrm{sub}}} \tag{18}\]  
the coupled plasmon modes of double layer graphene can be solved. Note that \(\sigma_{\mathrm{sub}}\) does not have to be equal to \(\sigma\) of the upper graphene layer because they do not necessarily have the same \(E_{\mathrm{F}}\) or \(\tau^{-1}\). For simplicity, here we set \(\sigma_{\mathrm{sub}}=\sigma\); then with \(\varepsilon_{\mathrm{sub}}\) given by Equation (18), Equation (15) becomes  
\[\tanh qd_{\mathrm{2}}=-\frac{2\Gamma}{\Gamma^{2}+1} \tag{19}\]  
which has been derived previously [47]. Equation (19) can also be derived by finding zeros of the linear-response function of the double layer graphene system [15]. The coupled plasmon modes are plotted in Figure 5b,c using Equations (7) and (19). As can be seen, the plasmon dispersion described by Equation (13) is strongly hybridized as the distance between two graphene layers \(d_{\mathrm{2}}\) decreases. We also see that similar to the case of a metal substrate, \(q_{\mathrm{l}}\) increases for the same \(\hbar\omega\) as \(\varepsilon_{\mathrm{2}}\) increases. In Figure 5b, the plasmon dispersion of the double-layer graphene is also calculated using \(\sigma_{\mathrm{PRA}}\) and plotted as the dotted curve. As in the case of graphene on a metal substrate, the Drude model is a good approximation for the plasmon dispersion in the low-\(q_{\mathrm{l}}\) region.
# Cooperative Three-View Imaging Optical Coherence Tomography for Intraoperative Vascular Evaluation
## 3 Results
### Rat Femoral Artery Imaging
Figure 13 shows the simultaneous structure and flow imaging results of a 1.2 mm outer diameter tube with milk flowing inside at pump rates of 0, 0.2, 0.4, and 0.6 mL/min, respectively. Please note that images are cropped here to show areas of interest. We can see clearly that when the pump rate was increased, the Doppler signals got stronger in each view. In addition, we can see the parabolic speed distribution profile within the inner lumen of the tube. Ideally, left and right view Doppler signals should have the same magnitude but opposite directions as their Doppler angles are complementary. However, during the fabrication and assembling process small folding mirror deviation from ideal position will cause a Doppler angle mismatch. That is why Figure 13 Doppler speed magnitudes for the left and right view are different. Based on the results of tube flow imaging, we can decrease the sampling distance to perform flow imaging of the vessel. In that case, the imaging frame rate will be reduced. Figure 13: Cooperative three-view structure and flow imaging results of a 1.2 mm outer diameter tube. (**A**) Structure image, (**B**) Doppler image with pump off, (**C**) Doppler image at a pump rate of \(Q\) = 0.2 mL/min, (**D**) Doppler image at a pump rate of \(Q\) = 0.4 mL/min, and (**E**) Doppler image at a pump rate of \(Q\) = 0.6 mL/min.
# Generative Modeling for Imbalanced Credit Card Fraud Transaction Detection
## 2 Related Work
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Ref.** & **Proposed Solution** & **Metrics** & **Key Findings** & **Limitations** \\ \hline \multirow{2}{*}{[22]} & Clustering-based & \multirow{2}{*}{Precision, Recall, F1} & \multirow{2}{*}{Enhances classifier performance} & \multirow{2}{*}{Limited to datasets with clear clusters} \\  & Noisy-Sample-Removed & & Score, Accuracy & & \\  & Undersampling (NUS) & & & \\ \hline \multirow{2}{*}{[23]} & K-means Convolutional & \multirow{2}{*}{Precision, Recall, F1} & \multirow{2}{*}{Achieves highest metrics, superior fraud detection} & \multirow{2}{*}{High computational complexity} \\  & GAN (K-CGAN) & & & \\ \hline \multirow{2}{*}{[24]} & XGBoost with Random & \multirow{2}{*}{Precision, Accuracy} & \multirow{2}{*}{High precision and accuracy, effective handling of class imbalance} & \multirow{2}{*}{May overfit on minority class} \\  & Oversampling & & & \\ \hline \multirow{2}{*}{[25]} & LSTM and GRU in Stacking & Sensitivity, \multirow{2}{*}{S Specificity} & Achieves sensitivity of 1.000 & Complex model, requiring \\  & Ensemble with MLP & & & and specificity of 0.997 & extensive tuning \\ \hline \multirow{2}{*}{[26]} & Deep Autoencoder for & \multirow{2}{*}{Precision, Recall, F1 Score} & Improves deep learning & Limited generalization to \\  & Representation Learning & F1 Score & classifier performance & non-financial data \\ \hline \multirow{2}{*}{[27]} & \multirow{2}{*}{HNN-CUHIT Framework} & \multirow{2}{*}{F1 Score} & \multirow{2}{*}{Outperforms traditional models, effective in imbalanced data handling} & \multirow{2}{*}{Requires specific data types and features} \\  & & & & \\ \hline \multirow{2}{*}{[28]} & Federated Learning & \multirow{2}{*}{Prediction Accuracy} & High accuracy with PyTorch, effective for federated learning & \multirow{2}{*}{Increased computational time} \\  & F frameworks & AUPRC (Area Under & & \\ \hline \multirow{2}{*}{[29]} & Autoencoders for & \multirow{2}{*}{\
\begin{tabular}{} \end{tabular} } & Improves classifier performance & Dependence on quality of \\  & Synthesizing Class Labels & & & \\  & Recall Curve & & & \\ \hline \multirow{2}{*}{[30]} & Real-time Fraud Detection & \multirow{2}{*}{
\begin{tabular}{} \end{tabular} } & Accuracy, Speciality, Sensitivity & High accuracy (>0.99), handles class imbalance & Complex real-time adaptation \\  & Logistic Regression & & & \\ \hline \multirow{2}{*}{[31]} & Graph Neural Networks & \multirow{2}{*}{Precision, Recall, F1 Score} & \multirow{2}{*}{Outperforms other models in precision, recall, and F1 score} & \multirow{2}{*}{High computational cost} \\  & (GNNs) for Feature Selection & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The Summary of the related work.
## 5 Safe and Accurate Aerial Manipulation
### Control Methods for Aerial Manipulation
#### 5.2.3 Active Disturbance Rejection Control  
The main idea of this strategy is to estimate online \(\mathcal{F}(\mathbf{x},\dot{\mathbf{x}},t)\) in some accurate manner [43]; thereafter, it is used as part of the feedback control action. The total disturbance can be estimated with a high-order observer such as the following:  
\[\begin{bmatrix}\dot{\mathbf{x}}_{1}\\ \dot{\mathbf{x}}_{2}\\ \dot{\mathbf{x}}_{3}\end{bmatrix}=\begin{bmatrix}\dot{\mathbf{x}}_{2}+\varepsilon \Phi_{1}\bigg{(}\frac{\mathbf{x}_{1}-\dot{\mathbf{x}}_{1}}{\varepsilon^{2}} \bigg{)}\\ \dot{\mathbf{x}}_{3}+\Phi_{2}\bigg{(}\frac{\mathbf{x}_{1}-\dot{\mathbf{x}}_{1 }}{\varepsilon^{2}}\bigg{)}+u\\ \frac{1}{\varepsilon}\Phi_{3}\bigg{(}\frac{\mathbf{x}_{1}-\dot{\mathbf{x}}_{1 }}{\varepsilon^{2}}\bigg{)}\end{bmatrix}, \tag{10}\]  
where \(\mathbf{x}_{1}=\mathbf{x}\) is the measured pose of the UAV, \(\dot{\mathbf{x}}_{i}\in\mathbb{R}^{6}\)\(i=1,2\), are the estimated states of the UAV, \(\dot{\mathbf{x}}_{3}\in\mathbb{R}^{6}\) is an estimated augmented state vector that contain the estimated lumped disturbance \(\dot{\mathbf{x}}_{3}\triangleq\hat{\mathcal{F}}(\mathbf{x},\dot{\mathbf{x}},t)\), \(\Phi_{i}:\mathbb{R}^{6}\rightarrow\mathbb{R}^{6}\) are functions that guarantee \(|\mathbf{x}_{1}-\dot{\mathbf{x}}_{1}|\to 0\) as \(t\rightarrow\infty\) and \(\varepsilon\) is a positive scalar that the smaller it is, the faster (10) converges.  
Thus, the feedback control that yields (9) despite disturbances and uncertainty affecting the UAV is the following:  
\[u=\mathbf{R}^{-1}\bar{\mathbf{M}}(u_{0}-\dot{\mathbf{x}}_{3}), \tag{11}\]  
where \(\bar{\mathbf{M}}\) is an approximation of \(\mathbf{M}\). Note that, if the mass or the inertia tensor of the UAV are not accurately known, an approximation of such values are suitable to be proposed. If they are completely unknown, a unit value is also a good alternative; the estimation of the total disturbance is even capable of compensating them.
## 2 Related Work
[PERSON] and [PERSON] [21] have investigated the performance of a cooperative relay selection on an IEEE 802.16j MMR WiMAX network. The authors used a MAX-MIN and Harmonic Mean relay selection criterion to analyze the system performance. Recent research has shown the significance of the log likelihood ratio in improving the performance of a communication system. Several researchers have also mentioned its importance in improving the performance of cooperative diversity systems. [PERSON] and [PERSON] [22] proposed an optimal antenna selection scheme to minimize bit error probability based on a log-likelihood ratio test. [PERSON] et al. [23] have suggested an LLR based adaptive relaying technique based on a selective decode and forward transmission strategy and provided expression for optimal LLR threshold for minimum average bit error rate (ABER) requirement. [PERSON] and [PERSON] [24] have provided a LLR threshold based relay selection and channel estimation scheme for cooperative decode and forward relaying technique using Binary Phase Shift Keying (BPSK) modulation. Most of the research work on the cooperative diversity approach does not contain the time-varying characteristics of fading channels. Since wireless propagation environment changes its nature depending on various things, it is always a good option to characterize a fading channel as a Markov chain with the limited number of stages based on an actual variation of the channel. [PERSON] and [PERSON] [25] were first to provide the application of the Markovian process in fading channels and have given the idea of a finite-state Markov channel (FSMC). Later [PERSON] and [PERSON] [26] formulated the finite state Markov channel for Rayleigh fading environment through partitioning a received SNR in different states. [PERSON] et al. [27] provided a generalized Markov model to represent flat fading environment with the impact of white Gaussian noise.
# An IoT-Based Solution for Intelligent Farming +
## 2 Related Work
### IoT/M2M Protocols and Communication Technologies
#### 2.1.1 Physical and Mac Layers  
IEEE 802.15.4 is still one of the most popular standards within the Physical and MAC layers. It was designed with three main requirements in mind: low consumption, low complexity and low cost [28]. Hence, it was adopted by several relevant protocols, such as ZigBee, ISA100 and WirelessHART. It operates mainly in the ISM band of 2.4 GHz, although it can also operate in the 868 MHz (in Europe) and 915 MHz (in the United States of America) bands. It supports data rates up to the 250 kbps and, concerning the MAC Layer, both Carrier Sense Multiple Access/Collision Avoidance (CSMA/CA) and Time Division Multiple Access (TDMA) (in the beacon-enabled mode) are supported. However, the literature reports significant limitations of the latter mode, particularly regarding the network formation [29] and mobility [30]. Besides IEEE 802.15.4, several alternatives emerged at the Physical Layer, but are not directly inter-operable with IP, requiring intermediate layers or Gateways. Examples are Z-Wave, designed particularly for domotics [31] and EPC-Global for RFID technologies [32].
## 5 Conclusions  
Previous studies have proved that constant control force exerted by Smart Spring is effective for vibration control of shafting. However, there are few researches on the improvement of control strategy. Based on ADAMS and MATLAB simulation software, this paper analyzes the lateral bending vibration control of the shaft system under critical state, and improves the control strategy of the intelligent spring and proposes a vibration suppression method based on function control force. The main conclusions are as follows:  
1. In this paper, the authors established a joint simulation model for the double-disk three-support shafting with Smart Spring, and carried out the open-loop control simulation and experimental study of the shafting over-critical vibration. The simulation results showed that the fixed control force exerted by the Smart Spring can greatly reduce the displacement response of the double-disk shaft system accelerating through the critical value. However, the fixed control force is not better when greater, but there is an optimal fixed control force making the vibration control effect the best. The optimal fixed control force is 425 N, and the vibration reduction rate is the maximum, which is 71.6%. The double-disk shafting test system was designed and tested. The test results showed that when the maximum control voltage is 150 V, the vibration reduction rate is the highest, reaching 44.2%, which verified that the Smart Spring support has good control effect on the lateral bending vibration of the three-support shafting when the acceleration is over-critical, and further proved the rationality of the joint simulation method.
2. The open-loop control of the spring system was carried out by the joint control function of the spring system. The simulation results showed that with the increase of fixed control force,  
Figure 24: Response curves of the shaft system under different control methods.  
Figure 25: Critical vibration peak value of shafting under different control methods.  
the first critical speed gradually increases and tends to be stable, and the peak value decreases first and then increases. The optimal fixed control force is 200 N, of which the maximum vibration reduction rate reaches 55.6%. Compared with the fixed control force, the displacement response of the function control force is significantly less than that of the fixed control force of 200 N, so it has a better vibration reduction effect.  
Conceptualization, M.-M.L. and L.-L.M.; methodology, M.-M.L.; software, M.-M.L.; validation, M.-M.L., L.-L.M. and C.-G.W.; data curation, L.-L.M.; writing--original draft preparation, L.-L.M.; writing--review and editing, M.-M.L.; supervision, C.-G.W.; project administration, R.-P.Z.; funding acquisition, R.-P.Z. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China (Grant No. 51775265), and the National Key Laboratory of Science and Technology on Helicopter Transmission (Nanjing University of Aeronautics and Astronautics) (Grant No. HTL-A-19G09).  
The authors declare no conflict of interest.
## 2 Research Methodology
### Cluster Classification and Feature Matching  
Regardless of the clustering technique employed, the proposed model classifies clusters into four distinct categories: (1) one-to-one, (2) one-to-many, (3) many-to-one, and (4) many-to-many. This classification strategy is based on the densities of \(X_{\text{Q}}\) and/through \(X_{\text{P}}\), as shown in Figure 12. For a given cluster X, \(X_{\text{Q}}\) and \(X_{\text{P}}\) denote the subset of features from Q and P within the cluster, respectively. Category 1 includes the direct correspondence of isolated pairs within their own clusters. Categories 2 and 3 encompass clusters of merging defects and newly detected defects, respectively. The matches and outliers within these clusters are identified using distance-based filtering strategies. Finally, Category 4 represents general clusters where point matching, such as the Voronoi model, is employed to identify matches and outliers.  
To account for features positioned near the unrolling reference point, double unrolling of \(X_{\text{P}}\), as proposed by [PERSON] [13] and shown in Figure 4, is performed prior to any further analysis for clusters in Category 2 and 3, resulting in a new extended set  
Figure 12: Outlier and cluster classification, illustrating the four density-based categories: (1) one-to-one, (2) one-to-many, (3) many-to-one, and many-to-many—example problem.  
\(X_{\widetilde{P}}=\left\{X_{\widetilde{P}_{k}}\in W,k=1,2,\ldots,2n_{X}\right\}\), where \(n_{X}\) is the size of the moving set in cluster X. For clusters in Category 4, double unrolling is performed based on the requirements of the utilized point matching method. For example, the annealing model always requires double unrolling, while the Voronoi model requires it only when features are present near the unrolling reference point.