esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions  
A smart city facilitates the life of its citizens by providing better services than non-smart cities. Due to the extensive presence of digital data, smart cities are also vulnerable to various types of attacks. Machine-learning-based cyber threat intelligence can secure smart city environments by monitoring attacks and analyzing data threats in order to take prevention measures. In this paper, we have proposed a hybrid deep learning model to classify threats. The proposed model uses a CNN and a QRNN to improve feature extraction, increases classification accuracy, and lower the FPR. We evaluated our model on the BoT-IoT and TON_IoT datasets, and our results showed the effectiveness of our model in improving classification accuracy and lowering the FPR. In addition, the results showed that the QRNN model could improve classification time performance while providing high accuracy and lower FPR than LSTM. Thus, the proposed model for CTI for smart cities can accurately analyze and classify data in real time.  
One of the limitations of this work is the authors' use of datasets. Due to the security and privacy of smart city citizens, it was difficult to evaluate the proposed model on real-time data. Additionally, for implementation, we evaluated the model as a centralized system. In future work, we can implement the proposed model in a distributed environment with parallel training to improve classification performance.  
Data curation, N.A.-T; Formal analysis, N.A.-T; Funding acquisition, N.A.S.; Investigation, N.A.S. and N.A.-T; Project administration, N.A.S.; Resources, N.A.S.; Supervision, N.A.S.; Validation, N.A.-T; Writing--original draft, N.A.-T. All authors have read and agreed to the published version of the manuscript.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Accuracy\%** & **TPR\%** & **FPR** \\ \hline MLP & 99.67 & 99.51 & 0.03 \\ \hline CNN & 99.88 & 99.75 & 0.01 \\ \hline GRU & 97.85 & 96.95 & 0.27 \\ \hline LSTM & 99.83 & 99.79 & 0.02 \\ \hline Our model & 99.99 & 99.99 & 0.001 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Comparison of our proposed model with other ML models based on TON_IoT dataset.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Accuracy\%** & **TPR\%** & **FPR** \\ \hline MLP & 99.98 & 86.42 & 0.002 \\ \hline CNN & 99.98 & 88.13 & 0.001 \\ \hline GRU & 99.98 & 96.06 & 0.001 \\ \hline LSTM & 99.99 & 94.69 & 0.0004 \\ \hline Our model & 99.99 & 99.92 & 0.0003 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Comparison of our proposed model with other ML models based on BoT-IoT dataset.  
Funding: The authors would like to thank SAUDI ARAMCO Cybersecurity Chair, Imam Abdulrahman Bin Faisal University for funding this project.  
The authors would like to thank [PERSON] and [PERSON] for their feedback on an earlier non-peer-reviewed version of the manuscript which was shared on the Arxiv repository.  
The authors declare no conflict of interest.
# Numerical Simulation of the Influence of Spur Dikes Spacing on Local Scour and Flow
## 2 Numerical Model
### Sedimentation Scour Model
#### 2.1.1 Bed-load Transport  
Bed-load transport is the mode of sediment transport due to rolling or bouncing over the surface of the packed bed of sediment.  
\[q_{b,i}=\Phi_{i}\Big{[}g\Big{[}g\Big{[}\frac{\rho-\rho_{f}}{\rho_{f}}\Big{]}A_{ i}^{3}\Big{]}^{\frac{1}{2}} \tag{1}\]  
\[\Phi_{i}=\beta_{i}(\theta_{i}-\theta_{cr,i})^{1.5}c_{b,i} \tag{2}\]  
where \(q_{b,i}\) is the volumetric bed-load transport rate in units of volume per width per time, \(\Phi_{i}\) is the dimensionless bed-load transport rate, \(g\) is gravity in absolute value, \(\rho_{i}\) is the mass density of sediment grains, \(\rho_{f}\) is the fluid density, \(d_{i}\) is the grain diameter, \(\beta_{i}\) is the bed-load coefficient, \(\theta_{i}\) is the Shields parameter, \(\theta_{cr,i}\) is the critical Shields parameter, and \(c_{b,i}\) is the volume fraction of species \(i\) in the bed material.  
\[\theta_{i}=\frac{\tau}{gt_{i}\big{(}\rho-\rho_{f}\big{)}},\ \theta_{cr,i}=\frac{c_{r,i}}{gt_{i}(\rho-\rho_{f})} \tag{3}\]  
where \(\tau\) is the bed shear stress, and \(\tau_{cr,i}\) is the critical bed shear stress.
## 2 Data and Method
### Data Processing
#### 2.4.2 Atmospheric Correction  
Atmospheric correction is the process of eliminating the radiation errors affected by the atmosphere [4; 41]. FLAASH was applied in this article for atmospheric correction, using ENVI (Environment for Visualizing Images) software based on the effects of radiation calibration. The standard moderate spectral resolution atmospheric tranmittance algorithm and computer model (MODTRAN) atmospheric model and the aerosol type of imagery can be used directly [42]. FLAASH, being on the basis of MODTRAN4 + radiative transmission model, has high accuracy. The FLAASH atmospheric correction tool avoids the measurement of atmospheric parameters that are synchronized with the image imaging [43]. It can obtain the estimation of atmospheric properties based on the spectral information of the pixels on the image maintaining a high degree of reducibility in the spectral information of the features, and obtain more accurate physical model parameters of the features [44; 45]. Furthermore, the application of the FLAASH model can also correct the \"neighborhood effect\" caused by diffuse reflection and perform spectral smoothing on spectral noise caused by artificial suppression [46].
## 3 A Security-Enhanced Authenticated Key-Agreement Protocol: SE-UAVAKA
### SE-UAVAKA Authentication Key-Agreement Phase
#### 3.4.4 Main Improvements of This Protocol  
* **Improved efficiency**: The protocol reduces scalar multiplication operations to four (two per entity), significantly lowering computational latency compared to prior works (e.g., [PERSON]'s eight operations).
* **Resistance to Known Session-Specific Attacks**: A key innovation lies in binding the session key to both long-term identities (\(ID_{A}\), \(ID_{B}\)) and ephemeral parameters (\(r_{a}\), \(r_{b}\)). Even if temporary values (\(r_{a}\), \(r_{b}\)) leak, an adversary cannot compute \(SSK_{AB}=H(r_{a}\cdot ID_{A}\cdot N_{b})\) without knowing \(ID_{A}\) and \(N_{b}\), which are protected via XOR obfuscation and encrypted channels. This contrasts with vulnerabilities in [PERSON]'s and [PERSON]'s protocols, where session keys become directly computable from leaked temporaries.
* **Mutual Authentication Guarantees**: The protocol enforces mutual authentication through bidirectional hash verification. Entity B validates \(H_{a}\) to confirm that \(N_{a}\) was generated by a legitimate UAV with knowledge of \(ID_{A}\) and \(y_{a}-x_{a}\). Similarly, Entity A verifies \(H_{b}\) to authenticate the ground station. This dual-check mechanism ensures that neither party can proceed to key derivation without successful authentication.  
By integrating these mechanisms, the SE-UAVAKA protocol achieves robust security with minimal computational burden, addressing the critical need for lightweight yet secure communication in UAV networks.
# Integrating Satellite and Ground Measurements for Predicting Locations of Extreme Urban Heat
## 3 Results
### Land Cover/Band Correlations  
Results from the correlation analysis between land classification-linked indices and raw Sentinel-2 bands indicate the strongest correlations between the Normalized Difference Vegetation Index (NDVI), which is a measure of total vegetation, and Normalized Built-up Area Index (NBAI), which is a measure of built up area (e.g., roads, buildings, driveways, etc.). The NDVI had the highest correlations to band 8 (\(R\) = 0.89), band 2 (\(R\) = \(-\)0.32), and band 4 (\(R\) = \(-\)0.29), while the NBAI had strong correlations to band 4 (\(R\) = 0.74), band 2 (\(R\) = 0.70), and band 3 (\(R\) = 0.69) (Table 4). The evaluation of each Sentinel band with the two remotely sensed indices suggests that the raw information contained in the Sentinel-2 bands reflect abstract descriptions of LULC.
## 2 Decomposition Methodology
### Brief Introduction to SVD  
Singular Value Decomposition (SVD) is an algorithm based on an orthogonal matrix transformation. The matrix \(A(m\times n)\), when processed by SVD, can be decomposed into three matrices with different dimensions, as shown in Equation (6):  
\[A=USV^{T} \tag{6}\]  
where \(U=[u_{1},u_{2},\dots,u_{1}]\in R^{m\times m}\) and \(V=[v_{1},v_{2},\dots,v_{1}]\in R^{n\times n}\). The notation \(S\) is a diagonal matrix with n singular values with descending order, showing as \(S=[diag(\lambda_{1},\lambda_{2},\dots,\lambda_{n}),0]\in R^{m\times n}\). Singular values from \(S\) contain the information from the original signal.
## 5 Multi-Feedback Generation Mechanism
### Further Discussion on Generation Mechanisms  
From the above, the contribution of the second term in Equation (18) will be reduced with the increasing number of feedback candidates for both generation mechanisms, and they will converge to the MAP algorithm. However, it requires a larger channel matrix and more candidates as in [14]. The main difference between the Gibbs sampling-based generation and the tree search-based generation is the terms \(\text{Pr}(\mathbf{s}_{\bar{k}}^{(b)})\) and \(\text{Pr}(\hat{\mathbf{s}}_{\bar{k}})\). For TSG, the probability \(\text{Pr}(\tilde{\mathbf{y}}_{k}^{(b)})\) is calculated with the LLR fed back from the channel decoder. On the other hand, each feedback in GSG is assumed to be equally probable. However, they perform almost identically in BER performance with the same number of feedback candidates. This is because TSG and GSG make use of the LLR from the channel decoder in different forms. The complexity of these two methods is not very intensive. The GSG employs one or multiple random number generators, which can be implemented efficiently without any multiply operations, as stated in [27]. For TSG, multipliers are also not required due to the use of logarithm domain computation. For example, the number of real additions required for TSG is around \(2 QB|\mathbb{C}|\) at most.
### Related Works
gave an overview of some of the most significant optimization problems arising in planning cellular networks [22] and provided an up-to-date view of challenges in modern design of wireless networks [23]. Moreover, the topic of joint optimization of power control and user association has been a very relevant topic of research in the mathematical optimization community working on wireless network design. [PERSON] et al. [24] presented a mathematical formulation for wireless network design purely based on binary user-base station assignment variables. Then [PERSON] et al. [25] presented a tight mathematical formulation and cutting plane algorithm for wireless network design based on the discretization of the power emission of transmitters that eliminates all numerical instabilities. Also they [26] proposed a genetic algorithm for better exploring the set of power configurations associated with power-indexed formulations introduced in the previous suggested reference. Moreover, many works have been devoted to develop effective models and efficient algorithms including power control and user association. Some more relevant works are introduced as follows. [PERSON] et al. [27] formulated the problem of maximizing different performance on RFB-based network architecture. [PERSON] et al. [28] proposed novel optimization models that allow the minimization of the energy of the computing and network infrastructure which is hosting a set of service chains that implement the VNFs. [PERSON] et al. [29] provided an assessment on what can be achieved with current technologies and gives a first confirmation of the validity of the proposed approach. [PERSON] et al. [30] designed an efficient algorithm, called P5G, which is based on Particle Swarm Optimization (PSO). [PERSON] et al. [31] introduced a fast decomposition heuristic for designing wireless mesh networks, including signal-to-interference constraints and user-hotspot association. [PERSON] et al. [32] proposed an effective branch-and-benders-cut algorithm for power minimization in wireless networks. However, these works do not properly address the energy efficient joint optimization problem as a fractional and mixed integer nonlinear programming (FMINLP) problem. We in contrast do this and, using the [PERSON]'s theorem, an approximately iterative method, and [PERSON]'s dual method, we propose a new algorithm with low complexity to solve the problem.
## 4 Discussion
### Effect of T6 Treatment  
The development of the COF as a function of sliding distance for the T6-treated ASFs is significantly different from their non-T6-treated counterparts (Figure 2). For all samples, there is an initial sharp increase in the COF at sliding distances of less than 50 m, followed by a sudden drop and a low constant beyond 50 m. However, the average COF values for the T6-treated samples indicate a similar trend as to what was observed for the non-T6 treated syntactic foams, where the samples with larger ASW particles have a higher COF (Table 2). The significant decrease in the COF, and thus specific wear, for the six treated samples is primarily due to the hardening of the Al matrix, which is well documented in the literature [35].  
The wear track maps of the T6-treated ASF samples show evenly worn surfaces, with less variability in height loss as the surface is worn when compared to the non-T6-treated samples (Figures 3-5). The wear parameters (Table 2) show a similar correlation with particle size to the non-T6-treated syntactic foam samples. Compared to the non-T6-treated syntactic foam samples, T6 treatment causes an overall enhancement in wear resistance due to the hardened Al matrix.
# Blockchain and Smart Contracts for Digital Copyright Protection
## 5 Proposed Protocol  
The experiences described in [9; 16] inspire the proposed watermarking protocol, whose main aim is to meet all the requirements reported in [1]. In this regard, the protocol exploits smart contracts and blockchain to generate the tokens used in the protection transactions and to lock the final purchase licenses in a public ledger. In particular, two specific smart contracts are executed during each transaction. The former generates a first \"secret\" block, which includes the tokens needed to start the purchase transaction and to apply the protection to the chosen content. In this phase, the block is not published by the blockchain but is kept secret and shared only by the buyer and the content provider involved in the transaction. The latter generates a final \"public\" block, representing the protected content's purchase license. It is published in the blockchain and built on the tokens in the secret block created by the former smart contract. The overall result is a protocol that works without TTP.  
Even though the proposed protocol can run without the centralized control of a TTP, it still needs a \"judge\" to implement the \"identification and arbitration protocol\". This protocol makes it possible to determine the identity of illegal distributors of copies of copyrighted digital content [9; 16]. The judge is a TTP, which cannot be considered a WCA since it plays a limited role in the protocol: it does not participate in the watermark embedding into the digital content distributed on the Internet [16; 17].  
The proposed protocol can be briefly described as follows: (1) the seller or content provider (\(\mathcal{CP}\)) releases only encrypted and watermarked digital contents; (2) the buyer (\(\mathcal{B}\)) decrypts the received content and thus obtains its copyrighted version; (3) the protection transaction is based on two smart contracts automatically executed within a blockchain (\(\mathcal{BC}\)); the contracts take charge of generating and managing all the security tokens needed to implement the transaction without resorting to a TTP; (4) the judge (\(\mathcal{J}\)) determines if a buyer has illegally distributed copyrighted contents.  
The protocol consists of the _protection protocol_ and the _identification and arbitration protocol_. Table 1 defines the symbols used to describe the protocols.
# Automated and Controlled System for Analysis of Residual Limbs Thermograms of Transtibial Amputees
## 3 Methodology
### Control Solution
#### 3.4.2 Tracking Trajectory Problem
: _Consider Assumptions 1 and 2, and the system (5) in closed-loop with the following control law,_  
\[u=N(\zeta_{u})\alpha_{u}, \tag{6}\] \[\alpha_{u}=(\gamma+(\phi_{2}^{+})^{2}/2)\bar{\Delta}_{3}+\dot{ \Phi}_{3}^{\top}q_{2,3}-\dot{v}_{2},\]  
_where \(N\colon\mathbb{R}\to\mathbb{R}\) satisfies Definition 1, the variable \(\zeta_{u}\) satisfies \(\dot{\zeta}_{u}=\bar{\Delta}_{3}\alpha_{u}\), with \(\gamma\) as a positive constant. Consider \(\dot{\Phi}_{3}^{\top}=[\dot{\phi}_{3},\dot{\phi}_{4}]\) the vector for estimated parameters \(\phi_{3}\) and \(\phi_{4}\), the vector \(q_{2,3}=[q_{2},q_{3}]^{\top}\). The auxiliary variables \(\bar{\Delta}=[\bar{\Delta}_{2},\bar{\Delta}_{3}]^{\top}\), \(\bar{\Delta}_{2}=\Delta_{2}-\
u_{1}\), \(\bar{\Delta}_{3}=q_{3}-\
u_{2}\), with \(\
u_{1}\) and \(\
u_{2}\) as stabilizing inputs for the two steps applying the back-stepping method,_  
\[\
u_{1}=-\gamma\Delta_{1},\ \
u_{2}=N(\zeta_{v})\alpha_{v}, \tag{7}\] \[\alpha_{v}=\gamma_{\phi}\bar{\Delta}_{2}+\dot{\phi}_{1}\sin{(q_{1 })}-h(q^{*})-\dot{v}_{1}+\frac{\Delta_{1}}{\Delta^{+}-\Delta_{1}^{2}},\]  
_where \(\gamma_{\phi}=\gamma+1/2\), \(\zeta_{v}\) satisfies \(\dot{\zeta}_{v}=\bar{\Delta}_{2}\alpha_{v}\), and \(\dot{\phi}_{1}\) is the estimated parameter for \(\phi_{1}\). The estimated parameters have the following adaptive law:_  
\[\dot{\Phi}=H(q)\bar{\Delta}-\gamma_{\Phi}\dot{\Phi},\] \[\text{with }H(q)= \begin{bmatrix}\sin{(q_{1})}&0\\ 0&q_{2}\\ 0&q_{3}\end{bmatrix} ,\dot{\Phi}= \begin{bmatrix}\dot{\Phi}_{1},\dot{\Phi}_{3}^{ \top}\end{bmatrix} ^{\top}.\]_If the initial condition for \(q_{1}\) satisfies \(q_{1}(0)\in\left(q^{*}(0)-\left(\Delta^{+}\right)^{1/2},q^{*}(0)+\left(\Delta^{+} \right)^{1/2}\right)\), then, the tracking error \(\Delta_{1}^{2}\leq\Delta^{+}\), \(\forall t\in\mathbb{R}^{+}\), \(\Delta_{1}\) will asymptotically converge, and \(\bar{\Delta}\) and the estimation error for the parameters will be ultimately bounded._  
Proof.
## 4 Characteristics of Different Time Distribution Systems
### Precision Time Protocol
#### 4.2.1 Time Distribution Mechanism  
The protocol is suitable for systems where devices communicate over networks, including Ethernet [82].  
PTP organizes clocks in a master-slave hierarchy, with the master clock serving as the time reference for the entire system, as illustrated in Figure 7. The protocol employs the Best Master Clock Algorithm to select the master clock, arranging the PTP network into a logical tree structure. The grandmaster clock acts as the tree's root, while slave clocks receive time synchronization messages over network links. Slaves adjust their clocks based on the time information exchanged in these messages [18]. Typically, the PTP grandmaster obtains its time information through a GPS receiver. PTP uses the request-response mechanism shown in Figure 8 to compute path delays within the network.  
The PTP master clock initiates synchronization by sending a **Sync** message, marked with a departure timestamp \(t_{1}\), which is timestamped upon arrival at the slave clock as \(t_{2}\). The slave then responds with a **DelayReq** message, departing at \(t_{3}\) and arriving back at the master clock at \(t_{4}\)[83]. These timestamps define the trip times: \(t_{ms}\) for the master-to-slave journey and \(t_{sm}\) for the slave-to-master journey.  
The slave calculates the round-trip path delay using the timestamps according to Equation (1). This delay is then used to compute the clock offset using Equation (2). By applying this offset, the slave adjusts its clock. Upon completing this process, the slave clock is synchronized with the master clock.  
\[\text{Path\_Delay}=\frac{t_{ms}+t_{sm}}{2}=\frac{(t_{4}-t_{3})+(t_{2}-t_{1})}{2} \tag{1}\]  
\[\text{Clock\_Offset}=(t_{2}-t_{1})-\text{Path\_Delay} \tag{2}\]The computation of the clock offset and propagation time relies on the assumption that the master-to-slave and slave-to-master propagation times are equal and remain constant throughout the synchronization process [82].
## 2 Materials and Methods
### Evaluation Methods
#### 2.2.3 Photoelectrochemical Analysis  
For the PEC experiment, \(CuO\) nanorods (NRs) were used as the WE, because of promising characteristics for sensor applications, particularly in the photoelectrochemical sensing technique. In particular, \(CuO\) exhibits semiconductive properties with a narrow bandgap, allowing it to utilize visible light for biosensing. This is advantageous compared to materials like zinc (II) oxide and tin (IV) oxide, which possess wider bandgaps and require UV light for excitation, which can be detrimental to biological elements in the test. Furthermore, \(CuO\) is a cost-effective and widely available material for the testing experiment. The CE and RE were the same as those used previously. The PEC experiment used the photoamperometry technique, where a constant potential of \(-0.2\) V was applied to the cell. The excitation light cycled on and off every 60 s with a pulse width of 30 s, following a quiet time of 150 s. The excitation wavelength was chosen based on the absorbance spectrum of \(CuO\) NRs.  
The PEC sensing process essentially requires a photoactive material where the reaction occurs on its surface after the excitation light is harvested. As mentioned, this report used \(CuO\) NRs as a photoactive material due to their semiconductive properties and their capability to harvest visible light. The \(CuO\) NRs were synthesized using the hydrothermal method. Briefly, a 50 mL solution of 0.1 M copper (II) nitrate (\(Cu(NO_{3})_{2}\)) (\(\geq\)99.5%, Merck KGaA, Darmstadt, Germany) was first prepared before adding 0.8 g of sodium hydroxide (\(NaOH\)) (\(\geq\)99%, Merck KGaA, Darmstadt, Germany). The pH of the mixture was adjusted to 7 using \(HNO_{3}\) (Merck KGaA, Darmstadt, Germany), and then, stirred for 30 min. After that, the prepared mixture was subjected to hydrothermal annealing at 180 \({}^{\circ}\)C for 18 h. The resulting mixture was rinsed and centrifuged to obtain the sample \(CuO\) NRs that were finally spray coated on to the \(ITO\) electrode. The properties (crystal structure, morphology, and absorbance spectrum) of the \(CuO\) NRs were also evaluated.