esa-sceva/satcom-chunk-collection huggingface dataset:
# Context-Aware Beam Selection for IRS-Assisted mmWave V2I Communications
## 6 Conclusions  
As new technologies constantly emerge to improve the future generation networks, some existing techniques in mobility management may become under-performing which directly challenge the overall network performance. Research interests in mobility management will be renewed to seek for improved solutions or novel solutions to integrate with the emerging technologies. The use of IRSs immediately challenge the beam-based mobility management by creating more complex multi-cell multi-IRS scenarios where multi-point  
Figure 6: The impact of the traffic light on connection time.  
beam switching will become a major problem and require new techniques and analytical approaches to maintain connectivity across the various network attachment points.  
In scenarios with large networks high density of BSs, IRSs, and vehicles, the action space expands to include multi-beam-IRS combinations. This can be accommodated by extending the reward table and leveraging spatial grouping of IRS regions to reduce complexity. The proposed C-MAB framework is also highly scalable and remains computationally feasible under increasing vehicle density. As more vehicles enter the network, the frequency of context-beam interactions rises, which accelerates convergence and enhances learning robustness. The algorithm remains efficient due to its context-conditioned structure, which naturally partitions the learning problem. Exploring more sophisticated coordination among IRS panels, including shared context features, is part of our ongoing and future research.  
Conceptualization, A.K. and H.L.; algorithm design, R.S.d.V. and A.K.; software, R.S.d.V. and A.K.; writing--original draft, A.K. and R.S.d.V.; visualization, R.S.d.V. and A.K.; writing--review and editing, H.L. and A.K. All authors have read and agreed to the published version of the manuscript. This research received no external funding.  
Not applicable.  
Data Availability StatementData is contained within the article.  
The authors declare no conflicts of interest.
## 4 Micro-Scale Analysis
### Scanning Electron Microscopy (SEM)  
The scanning electron microscope (SEM) test was carried out using a Japanese Zeiss SIGMA high-resolution field emission scanning electron microscope, which mainly consists of an electron optical system, a signal collection and processing system, an image display and recording system, a vacuum system, a power supply and a control system, etc. The resolution of the secondary electron image is approximately 5-10 nm, and the resolution of the backscattered electron image is approximately 50-200 nm. Figure 17 shows the steps of the scanning electron microscopy test. According to the dynamic triaxial and nuclear magnetic resonance test, which was performed for sisal fibre doping \(\geq\)1.2%, the structural strength of the soil body weakened with the increase in sisal fibre incorporation. So, the SEM test was conducted using two sisal-modified soils (1.2% and 1.4% sisal). Scanning electron microscopy was used to observe the soil particles, pore sizes and microstructure. To better explore the changes in the pores and structural characteristics, the electron microscope observation times were increased 1000 times, as shown in Figure 18. Figure 18a shows that, when the sisal fibre doping was 1.2%, the soil changed from the original large soil particles into smaller soil particles, and the bulk pores gradually changed to a wider distribution of small pores. When the sisal fibre doping reached 1.4%, as shown in Figure 18b, with the increase in sisal fibre doping, the internal structure of the soil body changed, part of the cross-section of the soil body contained more granular and spherical particles, and large or rough soil particles gradually cemented together to become larger particles of the soil block. Thus, the soil particles could not be better cemented between the filling [31], and  
Figure 16: T\({}_{2}\) distribution curves of soil-5% EPS mixtures with different sisal contents and common clay.  
the fine pores gradually changed into isolated larger pores that were spaced apart. This showed that too much sisal fibre led to pores that were more angular and more complex, and the soil was less homogeneous.
## 3 Results and Discussion
### Electrochemical Characterization of IIP/Fe\({}_{3}\)O\({}_{4}\)@SiO\({}_{2}\)/MCPE Sensors
#### 3.2.5 Electrochemical Impedance Diagram  
The results of EIS were represented by Nyquist plots (Figure S3) and analyzed using the Randles equivalent circuit (inset of Figure S3), where Z\({}^{\prime}\) and Z\({}^{\prime\prime}\) were the real and imaginary parts of the impedance, respectively. As presented in Figure S3, the electrochemical impedance data were fitted using Zview software to acquire the R\({}_{\text{ct}}\) values for the different electrodes. The charge transfer resistance (R\({}_{\text{ct}}\)) value of the bare MCPE was 2207 \(\Omega\) (Figure S3e). When the bare MCPE was coated with MIP/Fe\({}_{3}\)O\({}_{4}\)@SiO\({}_{2}\), the R\({}_{\text{ct}}\) value decreased to 1684 \(\Omega\) (Figure S3d), indicating that the successful construction of the conductive copolymer layer promotes charge transfer, which is due to the acceleration of the charge transfer process by the magnetic field [51]. After the formation of the IIP ion imprinted membrane, the ion imprinted membrane was formed by hydrolysis and condensation under the condition where UO\({}_{2}\)\({}^{2+}\) serves as a template in the presence of binding ligand and crosslinker, and then removed the UO\({}_{2}\)\({}^{2+}\). The R\({}_{\rm ct}\) value is correspondingly reduced from 1684 \(\Omega\) (MIIP/Fe\({}_{3}\)O\({}_{4}\)@SiO\({}_{2}\)/MCPE with template) (Figure S3d) to 518 \(\Omega\) (MIIP/Fe\({}_{3}\)O\({}_{4}\)@SiO\({}_{2}\)/MCPE without template) (Figure S3c), objectively revealing the successful construction of the MIIP/Fe\({}_{3}\)O\({}_{4}\)@SiO\({}_{2}\)/MCPE channel. When the MIIP/Fe\({}_{3}\)O\({}_{4}\)@SiO\({}_{2}\)/MCPE with template ion removed were immersed in \(1.5\times 10^{-7}\) mol L\({}^{-1}\) UO\({}_{2}\)\({}^{2+}\) solution, the imprinting holes were occupied by the template ions. Therefore, the R\({}_{\rm ct}\) value greatly increases significantly from 518 \(\Omega\) to 905 \(\Omega\) (Figure S3b), meaning that some of the channels through which the redox probe passes were blocked. It turned out as expected, the N-MIIP/Fe\({}_{3}\)O\({}_{4}\)@SiO\({}_{2}\)/MCPE R\({}_{\rm ct}\) value of 2510 \(\Omega\) (Figure S3f) was much larger than the R\({}_{\rm ct}\) value of the removal of template ions since the N-MIIP/Fe\({}_{3}\)O\({}_{4}\)@SiO\({}_{2}\)/MCPE lacks such channels for the probe to pass through. The difference in R\({}_{\rm ct}\) values between MIIP/Fe\({}_{3}\)O\({}_{4}\)@SiO\({}_{2}\)/MCPE and MIIP/MCPE (775 \(\Omega\), Figure S3a) revealed that the Fe\({}_{3}\)O\({}_{4}\)@SiO\({}_{2}\) of the modified electrode can promote the charge transfer of probe Fe (CN)\({}_{6}\)\({}^{3-4-}\).
## 4 Analysis Results  
FE analyses are conducted to investigate the behavior of the adhesively connected pultruded GFRP bridge decks subjected to wheel loads at SLS and ULS. The global deflection of the deck and the local deformation at the joints are evaluated in Sections 4.1 and 4.2, respectively. To verify the safety of the joints, local stresses at the joints are evaluated to check against failure, as presented in Sections 4.3-4.6.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Description**} & \multirow{2}{*}{**Ulimate Strength**} & **Partial Safety Factor** & **Design Strength** \\ \cline{3-5}  & & Tensile & 153 MPa \({}^{*}\) & 3.16 & 48.4 MPa \\ \cline{3-5}  & & Compressive & 159 MPa \({}^{*}\) & 3.16 & 50.3 MPa \\ \cline{2-5}  & \multirow{2}{*}{Web} & Tensile & 146 MPa \({}^{*}\) & 3.16 & 46.2 MPa \\ \cline{3-5}  & & Compressive & 103 MPa \({}^{*}\) & 3.06 & 32.6 MPa \\ \hline \multirow{2}{*}{Adhesive} & Tensile & 6.2 MPa & 2.34 & 2.65 MPa \\ \cline{2-5}  & Shear & 9.8 MPa & 2.34 & 4.19 MPa \\ \hline \hline \end{tabular} \({}^{*}\)Tested values for the deck specimens [1,3].  
\end{table}
Table 10: Design strengths for the deck substrate and adhesive.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Description** & **Property or Value** \\ \hline Types & Two-part room temperature epoxy \\ \hline Model No. & HTA-210 \\ \hline Tensile strength \({}^{*}\) & 6.2 MPa \\ \hline Shear strength & 9.8 MPa \\ \hline Elastic modulus \({}^{*}\) & 88 MPa \\ \hline Max. strain (elongation) & 7.0\% \\ \hline \({}^{*}\)Test results after 30 d curing at room temperature (20 \(\pm\) 5 \({}^{\circ}\)C). \\ \hline \end{tabular} \({}^{*}\)Tested results after 30 d curing at room temperature (20 \(\pm\) 5 \({}^{\circ}\)C).  
\end{table}
Table 9: Properties of the adhesive.  
Table 11 summarizes the maximum deck deflections. They are 7.19 mm and 9.34 mm under FX and SS and LC1, which can be represented as L/348 and L/268, respectively, for the 2500 mm span. The maximum deflections of the deck under LC2 are 5.08 mm and 6.84 mm in the FX and SS, which can be represented by L/492 and L/366, respectively. The deck under LC1 deflects more than that under LC2. Because the double-wheel loads are sufficiently separated, the deformations of the two adjacent wheel loads do not interfere during weak-axis bending. Thus, none of these GFRP decks satisfies the serviceability deflection limit for vehicular bridges (L/800) shown in Table 2. Therefore, it is necessary to increase the flexural stiffness or decrease the span in strong axis.
# Fusion of Land-Based and Satellite-Based Localization Using Constrained Weighted Least Squares
## 4 Ccrlb
### Ccrlb for Land-Based Station  
Under the Gaussian noise assumption, the log-likelihood function of \(\mathbf{z}^{o}\) with respect to \(\mathbf{\xi}_{1}=\Big{[}\mathbf{u}^{\text{T}},(\mathbf{h}^{o})^{\text{T}}\Big{]}^{\text{T}}\) can be expressed as [20; 21]  
\[\ln(p\{\mathbf{z}^{o}|\mathbf{\xi}_{1}\})=\lambda_{a}-\frac{1}{2}(\mathbf{z}-\mathbf{z}^{o})^{ \text{T}}\mathbf{Q}_{\mathbf{z}}^{-1}(\mathbf{z}-\mathbf{z}^{o})-\frac{1}{2}(\mathbf{h}-\mathbf{h}^{o} )^{\text{T}}\mathbf{Q}_{\mathbf{h}}^{-1}(\mathbf{h}-\mathbf{h}^{o}) \tag{37}\]  
\(\lambda_{a}\) is a constant. The corresponding FIM is denoted as  
\[FIM_{DOA}=\text{E}\bigg{(}\frac{\partial\ln(p\{\mathbf{z}^{o}|\mathbf{\xi}_{1}\})}{ \partial\mathbf{\xi}_{1}}\frac{\partial\ln(p\{\mathbf{z}^{o}|\mathbf{\xi}_{1}\})}{\partial \mathbf{\xi}_{1}^{\text{T}}}\bigg{)}=\begin{bmatrix}\mathbf{F}_{1}^{\text{DOA}}&\mathbf{F }_{2}^{\text{DOA}}\\ \Big{(}\mathbf{F}_{2}^{\text{DOA}}\Big{)}^{\text{T}}&\mathbf{F}_{3}^{\text{DOA}}\end{bmatrix} \tag{38}\]where  
\[\left\{\begin{array}{l}F_{1}^{DOA}=\left(\partial z^{o}/\partial u^{\rm T}\right)^ {\rm T}\mathbf{Q}_{z}^{-1}\left(\partial z^{o}/\partial u^{\rm T}\right)\\ F_{2}^{DOA}=\left(\partial z^{o}/\partial u^{\rm T}\right)^{\rm T}\mathbf{Q}_{z}^{-1 }\left(\partial z^{o}/\partial(h^{o})^{\rm T}\right)\\ F_{3}^{DOA}=\left(\partial z^{o}/\partial(h^{o})^{\rm T}\right)^{\rm T}\mathbf{Q}_{ z}^{-1}\left(\partial z^{o}/\partial(h^{o})^{\rm T}\right)+\mathbf{Q}_{h}^{-1}\end{array}\right. \tag{39}\]  
The CRLB for DOA-based localization can be given as  
\[\mathbf{CRLB}_{DOA}=\left(\mathbf{FIM}_{DOA}\right)^{-1}=\begin{bmatrix}\mathbf{X}_{1}^{ DOA}&\mathbf{X}_{2}^{DOA}\\ \left(\mathbf{X}_{2}^{DOA}\right)^{\rm T}&\mathbf{X}_{3}^{DOA}\end{bmatrix} \tag{40}\]  
where \(\mathbf{X}_{1}^{DOA}\in\mathbb{R}^{3\times 3}\) gives the CRLB for source localization.  
Since the source position vector satisfies (2), the CCRLB with the source constraint can be expressed as [9]  
\[\mathbf{CCRB}_{DOA}=\mathbf{CRLB}_{DOA}-\mathbf{CRLB}_{DOA}\mathbf{\tilde{\Lambda}}\left(\mathbf{ \tilde{\Lambda}}^{\rm T}\mathbf{CRLB}_{DOA}\mathbf{\tilde{\Lambda}}\right)^{-1}\mathbf{ \tilde{\Lambda}}^{\rm T}\mathbf{CRLB}_{DOA} \tag{41}\]  
where \(\mathbf{\tilde{\Lambda}}=\left[\mathbf{\Lambda}_{1}\mathbf{u}\right]^{\rm T}\).
## 3 Case Studies  
In this section, two case studies have been analysed to evaluate the accuracy of the simplified approach with reference to the existing buildings.  
Figure 5: Accuracy of simplified approach in the prediction of \(\delta_{c}\).  
Figure 6: Accuracy of simplified approach in the prediction of \(\delta_{D}\).  
The first building is a Moment Resisting Frame belonging to a five-storey building consisting of five symmetrical bays of 4.00 m in each direction. The building was designed according to a pre-1970 design code. The reference site has been hypothesised in L'Aquila (high seismic risk area in Italy \(a_{g}/g=0.261\) for a return period of 475 years), and it is characterised by a type B soil and a topographic category T1. The floors have been designed to withstand a variable load of 2.00 kN/m\({}^{2}\) and a permanent non-structural load of 2.00 kN/m\({}^{2}\). The inter-storey height is 3.00 m, the thickness of the floors is 140 mm, and the total height of the building is 15.00 m. The weight per unit volume of concrete is assumed to be 24.00 kN/m\({}^{3}\). The beams are IPE300 sections, while the columns are HEA400 sections, both of S355 (\(f_{y}=355\) MPa). Figure 7a depicts the planimetric configuration of the building and the tributary area of the analysed frame, while Figure 7b indicates the designed cross-sections and the seismic forces.  
Figure 7: Plan configuration with tributary area (**a**); frontal view of the frame (**b**).  
The second case study is an MR Frame belonging to a three-storey building, as reported in Figure 8, whose plan configuration is depicted in Figure 9a.  
The building, located in Amatrice, Italy, was built some decades ago, before the introduction of modern seismic design standards. The building was damaged during the Central Italy earthquakes (24 August 2016 and 30 October 2016). The building lies on type B soil in the topographic category T1 (Han
# Internet of Vehicles for Sustainable Smart Cities: Opportunities, Issues, and Challenges
## 2 Global Market/Bibliographic Data Analysis and Standards
### Standards
#### 2.1.4 European Telecommunications Standards Institute (ETSI) ITS-G5 (ITS--Vehicular Communications)  
In Europe's pursuit of advancing ITS through established vehicular communication systems, the ETSI ITS-G5 plays a crucial role. In addition to its function in developing cooperative ITS applications, it deals with significant issues related to secure message formats and strong networking protocols. The broad use of ITS-G5 guarantees consistent methods for vehicle communication and promotes improvements in traffic safety and management along with connecting car technologies within the smart transportation system in Europe [46].  
1.5 Society of Automotive Engineers (SAE) J2735 (Dedicated Short-Range Communications Message Set Dictionary)  
By producing SAE J2735 the SAE has standardized key messages crucial for DSRC and supported the generation of a seamless connection among vehicles. This standard creates a uniform collection of message components that guarantees both uniformity and better compatibility in different manufacturers' systems. This standard gain significant support for building a foundation for V2V and V2I exchanges that ensures increased safety and reliability in transportation systems [47].
# An Efficient Palette Generation Method for Color Image Quantization
## 3 The Proposed Method
### Fast K-Means Algorithm  
The K-means algorithm is the common data clustering method. However, the K-means algorithm is time consuming. In order to reduce the computational cost, the fast K-means algorithm is developed.  
The data sampling process is employed, and many sampling rates are used in this study. The sampling rate = 1 indicates that every pixel of the color image is sampled. For the 2 \(\times\) 2 block shown in Figure 4a, only one pixel is sampled from the 2 \(\times\) 2 block if the sampling rate = 0.25. In this study, a random number, denoted by \(rnum1\), is generated. If the value of (\(rnum1\%\) 4) is equal to 3, then the location of the sampled pixel is (3/2, 3% 2) = (1, 1). For the 4\(\times\)4 block shown in Figure 4b, two pixels are sampled from the 4 \(\times\) 4 block if the sampling rate = 0.125. In this study, a random number, denoted by \(rnum2\), is generated. If the value of (\(rnum2\%\) 8) is equal to 5, then the location of the first sampled pixel is (\(x1\), \(y1\)) = (5/4, 5% 4) = (1, 1) and the location of the second sampled pixel is (\(x2\), \(y2\)) = (\(x1\) + 2, (\(y1\) + 2)% 4) = (3, 3).  
According to the data sampling process described above, the sampling rates 0.5, 0.0625 and 0.03125 can also be used. Two pixels are sampled from the 2 \(\times\) 2 block if the sampling rate = 0.5, only one pixel is sampled from the 4 \(\times\) 4 block if the sampling rate = 0.0625, and two pixels are sampled from the 8 \(\times\) 8 block if the sampling rate = 0.03125.  
The steps of the fast K-means algorithm can be described as follows.  
Input: The initial palette described in Section 3.1.  
Step 1: The data sampling process is employed. The sampled color pixels are denoted by \(SCP_{i}\), \(i\) = 0, 1,..., \(SPN\)--1.  
_Iter_ = 0  
StopF = 0  
Step 2: For the sampled pixel \(SCP_{i}\), the closest color in the palette is denoted by \(CCP_{i}\). The color \(CCP_{i}\) is efficiently determined by the Wu-Lin method described in Section 2. If the  
Figure 4: (**a**) Only one pixel is sampled, (**b**) two pixels are sampled  
index value of its closest palette color is \(r\), then the sampled pixel is classified into the \(r\)-th group.  
Step 3: Compute the mean values of these \(K\) groups. The new color palette is generated by sorting these \(K\) values in ascending order of length.  
Step 4: The mean square error at iteration \(Iter\) is defined as follows  
\[MSE1(Iter)=\frac{1}{SPN}\sum_{i=0}^{SPN-1}SED(SCP_{i},\ CCP_{i}). \tag{7}\]  
If ((\(Iter>\)0) and (\(MSE1(Iter)\geq\ MSE1(Iter-1)\))), then set \(StopF\) to 1.  
Step 5: \(Iter=Iter+1\).  
If ((\(Iter==Max\_cycle\)) or (\(StopF==1\))), then go to Step 6. Otherwise, go to Step 2.  
Step 6: The color palette is generated. The required iteration number, denoted by \(Iter\), is output.
## 5 Discussion
### PSD Changes under Different Freezing Gap Times at \(-20\)\({}^{\circ}\)C  
Figure 15 shows the percent change in pore size at different gap times between two freezing actions. After the first freeze-thaw cycle in Figure 16a, the features and regularity were obvious for the curves with different thawing times. With time, the ice from free water began to melt and the pores in the range from 0.01 to 0.03 \(\upmu\)m and 1 to 9 \(\upmu\)m increased. Unfreezing water from capillary water fused with the melt freezing water to be detected as larger pores so the number of pores from 0.03 to 0.2 \(\upmu\)m decreased while the number of pores from 0.2 to 0.7 \(\upmu\)m increased. The upturn process of the subpeak was broken when the gap time exceeded four hours, indicating that the second freezing more severely influenced the almost thawed soft clay.  
From the above result, the pore with the size larger than 0.03 \(\upmu\)m plays a key role in the structural failure of soft clay, so the pore size changes between 0.03 and 0.2 \(\upmu\)m can reflected the influence of gap time.  
Figure 16: (a,b) PSD change at different freezing gap times.  
Figure 15: (a,b) Fitting curve of the peak value of freezing and thawing duration.  
Figure 16 shows that, after a double extend thaw collapse time, the secondary freezing action brought a very different change compared with the single freeze-thaw cycle. As the interval time increased, the proportion of small pores decreased slightly, and the proportion of mesopores increased rapidly.  
When the gap time was short, the ice in the soft clay entered the frozen state again without completely melting. When the gap time was long enough, the ice in the soft clay was completely melted, and the melted soft clay would undergo an entire secondary freezing.  
In summary, the degree of damage to the soft clay caused by the secondary freezing under different gap times was various. There is a certain time for thawing progress, when the gap time exceeds this value, the gap time increase has little influence on the thawed soft clay. In the actual construction, the interruption should be stopped before the main thawing effect occurs.
# IoT Intrusion Detection Taxonomy, Reference Architecture, and Analyses
###### Abstract  
This paper surveys the deep learning (DL) approaches for intrusion-detection systems (IDSs) in Internet of Things (IoT) and the associated datasets toward identifying gaps, weaknesses, and a neutral reference architecture. A comparative study of IDSs is provided, with a review of anomaly-based IDSs on DL approaches, which include supervised, unsupervised, and hybrid methods. All techniques in these three categories have essentially been used in IoT environments. To date, only a few have been used in the anomaly-based IDS for IoT. For each of these anomaly-based IDSs, the implementation of the four categories of feature(s) extraction, classification, prediction, and regression were evaluated. We studied important performance metrics and benchmark detection rates, including the requisite efficiency of the various methods. Four machine learning algorithms were evaluated for classification purposes: Logistic Regression (LR), Support Vector Machine (SVM), Decision Tree (DT), and an Artificial Neural Network (ANN). Therefore, we compared each via the Receiver Operating Characteristic (ROC) curve. The study model exhibits promising outcomes for all classes of attacks. The scope of our analysis examines attacks targeting the IoT ecosystem using empirically based, simulation-generated datasets (namely the Bot-IoT and the IoTID20 datasets).  
a 2021215 August 20  
Methods and Tactics (M&T). For example, the Mirai botnet caused large-scale Distributed Denial of Service (DDoS) attacks by exploiting IoT devices. While amplifying DDoS, these recent attacks utilize spoofed-source IP addresses to circumvent current solutions targeted to the Mirai botnet M&T. These solutions have motivated newer, more sophisticated attacks that are more complex and more destructive than the original Mirai botnet attributed attacks. Therefore, investigating effective IoT security countermeasures remains a research priority.  
IDSs are one promising avenue for monitoring IoT environments and are mainly effective at the network level. IDSs deployed in IoT environments analyze network data packets and generate real-time responses. To be effective, these IDSs need to operate under stringent IoT conditions of low energy, low process capacity, fast response, and notably huge volumes of data processing. Thus, enhancing IoT embedded IDSs is a continuous and serious issue requiring a significant understanding of the security vulnerabilities of IoT systems.