esa-sceva/satcom-chunk-collection huggingface dataset:
# Structural Health Monitoring of 2D Plane Structures
## 2 Structural System Identification of Structures with 2D Elements by Observability Techniques
### Proposed Algorithm  
To illustrate the application of the procedure presented above, an algorithm is proposed in this section for the structural system identification of 2D element structures with the OM. This algorithm is presented in Figure 1. The whole package of the 2D observability technique was written in MatLab [40]. This package contains two main connected parts. The first part contains only direct analysis codes. In this part, 2D finite element analyses were developed for the plane strain analysis and the user only needs to input the node coordinates, the mechanical properties, the boundary conditions, and the applied forces. Afterwards, through the interface the user selects the defined structure and runs the direct analysis to calculate the deflections at the free nodes and the reactions at the boundary conditions. Some of the information calculated in this step is used as input data for the inverse analysis of the structure in the second part. The second part of the package was only assigned to inverse analysis and contains the procedure presented in the preceding section. To carry out the inverse analysis, the user has to define the condition of the inverse analysis through the interface. In this step, the interface offers all the possible measurements of the structure calculated in the direct analysis. After selecting the measured degrees of freedom, an observability analysis is performed and the observable variables are provided together with their numerical values. The steps of the algorithm for the inverse analysis of the structure are as follows:  
* Step 1. Generate the classical stiffness matrix equation presented in Equation (2).
* Step 2. Exert the change in variable to the stiffness matrix using the functions of Poisson's ratio presented in Equations (7) and (8).
* Step 3. Establish Equation (3) considering the variables defined in Step 2.
* Step 4. Reorder the columns in matrix to isolate the monomial products of variables.
* Step 5. Introduce the boundary conditions, the known forces, and the measured deflections.
* Step 6. Reorder the system following Equation (5).
* Step 7. Calculate the null space of [C] numerically and identify the observed variables.
* Step 8. Calculate the particular solution of the system numerically using the Moore-Penrose pseudoinverse function.
* Step 9. Use the observed parameters or the observed coupled variables to simplify the other coupled variables.
* Step 10. Introduce the observed parameters into Step 5. Repeat until no additional parameters are observed (end of the recursive process).  
A summary of the aforementioned algorithm is illustrated in Figure 1.
## 4 Classification of Experimental Results and Analysis
### Introducing the Training Results of the Attention Mechanism Model  
In magnitude classification, an attention mechanism was introduced for model optimization. Comparative experiments were conducted using the AlexNet, VGGNet, and ResNet networks with different attention mechanisms integrated. The classification evaluation metrics of the experimental results are presented in Figure 8, which shows the respective classification performance of residual networks incorporating the SE attention mechanism and ECA attention mechanism.  
As observed from the figure, the model's accuracy, recall, and F1-score all improved after the introduction of the attention mechanism.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Precision\%** & **Accuracy\%** & **Recall\%** \\ \hline CNN & 67.8 & 63.5 & 62.1 \\ Alexnet + TL-M3 & 87.5 & 86.6 & 83.9 \\ VGGnet + TL-M3 & 88.3 & 87.3 & 87.1 \\ ResNet18 + TL-M1 & 84.5 & 82.9 & 81.1 \\ ResNet18 + TL-M2 & 90.1 & 90.0 & 90.8 \\ ResNet18 + TL-M3 & 92.7 & 91.2 & 90.8 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Results table of magnitude classification indicators.  
Figure 8: Evaluation index plot of SE attention mechanism and ECA attention mechanism in ResNet model classification.  
Figure 9 illustrates the performance of four models trained with the CBAM under the following training configuration: a learning rate initialized at 0.001 with step-wise decay (reduced by 0.1 every 10 epochs), a batch size of 32 (optimized for NVIDIA RTX 3090 memory constraints), 50 total epochs with early stopping triggered after 5 consecutive epochs without a validation accuracy improvement (actual convergence occurred at \(\sim\)30 epochs), and the Ranger optimizer with default parameters (\(\beta_{1}\) = 0.9, \(\beta_{2}\) = 0.999, weight decay = 1 \(\times\) 10\({}^{-5}\)). As shown, the CBAM-enhanced models demonstrate improved performance by integrating both spatial and channel attention modules. The training curves exhibit a similar shape to those of the transfer-learned ResNet18, but the CBAM approach achieves superior generalization and higher test set accuracy. Specifically, the training accuracy stabilizes around 30 epochs, aligning with the early stopping criterion and indicating efficient convergence under the specified hyperparameters.
# Smart Intersections and Connected Autonomous Vehicles for Sustainable Smart Cities: A Brief Review
## 1 Introduction
This article gives an overview of the state of the art of these intersections, with the following main objectives:  
* Highlights the key technologies underpinning smart intersection development that facilitate traffic optimization based on AI, real-time Data Analytics, and V2X communication systems;
* Reveals approaches for analysis, including simulation models, optimization methods, and real-world deployment case studies, to evaluate smart intersections' effectiveness;  
Figure 1: Trends in scientific publications and citation dynamics over time: (**a**) smart roads and (**b**) smart intersections (data from [9]). * Investigate the smart intersection ecosystem within smart cities, including its applicability in all transport modes, sustainability, and digital governance solutions;
* Analyze the top challenges for these cyber-physical intersections, including but certainly not limited to cybersecurity threats, generic standard hurdles, and scaling advanced intersection technologies in internet-limited regions;
* Analyze the future research directions for smart intersections, with a focus on AI-based adaptive traffic control, digital twins, and next-generation mobility systems. As a comprehensive and more holistic analysis, this review could be a useful summary of the state of the art for researchers and decision-makers in solving smart intersection deployment challenges across various urban contexts.
# A Secure Blockchain-Based Authentication and Key Agreement Scheme for 3 GPP 5G Networks
## 6 Performance Evaluation  
Most UE has limited computational resources and battery life. Additionally, the network resources in the 5G network are always finite. Hence, a good authentication scheme should have low computational overhead, communication overhead, and energy consumption. In this section, we analyze the performance of the 5 GSBA from these three aspects. Additionally, we evaluate its effectiveness against DoS/DDoS attacks using two simulation experiments. For the comparison, 5G-AKA [4] is selected because it is the 3 GPP standardized protocol. SE-AKA [20] is chosen because it supports perfect forward secrecy with the similar security functions as the 5 GSBA. Another blockchain-based 5G authentication protocol, BB-AKA 5G [10] is also included to show that the 5 GSBA can achieve a better performance than the existing blockchain-based scheme.
## 2 Message Replication in OppNets for Sensed Data Collection
### Current State of Message Replication in OppNets
#### 2.4.1 OppNet Movement Scenarios and Simulation Set-Up  
The Helsinki simulation area in the Opportunistic Network Environment (ONE) simulator [61], which consists of four main districts, is modified to obtain three movement scenarios, as shown in Figure 3. For comparative analysis the scenarios, namely small, medium, and large, correspond to their relative size. To obtain the different network sizes, the Helsinki simulation area is used to represent the medium scenario, the small scenario is represented by one of the four main districts in the medium scenario and the large scenario is artificially generated from the combination of 4 copies of the medium scenario. The number of nodes for each scenario is varied proportionally leaving the essential encounter characteristics unchanged.  
Figure 3: Movement scenarios used for investigating the impact of increasing node population and spatial locality on the performance of OppNet replication techniques (partitioned by a grid, the small, medium and large scenario consists of 1 region, 4 regions and 16 regions, which also corresponds to low, medium and high degree of spatial locality, respectively).  
It is assumed that communications are between mobile users in a city using smartphones or similar handheld devices equipped with GPS and moving according to the Working Day Movement (WDM) model [62]. Since WDM models daily movement routines in working days, a total simulation duration of 5 days is chosen, i.e., 1 day for warm up (to ensure complete encounter history generation), 3 days for message generation and routing, and 1 day for cool down. A message is generated every 10 to 15 min from a randomly chosen node (the source) to another randomly chosen node (the destination).  
Energy consumption for Bluetooth is according to the configuration settings in the module proposed by [PERSON] et al. [63]. Since we are interested in only energy expenditure of different replication techniques, only energy consumed from receiving and sending messages is considered and other means through which nodes consume energy is ignored. On that basis, scan energy (i.e., energy consumed from device discovery), scan response energy (i.e., energy consumed from device discovery response), and base energy (i.e., energy consumed in idle state) is set to 0. The parameters used for the simulation set-up are shown in Table 1. The following metrics are used to evaluate the performance of the network: (i) throughput, the percentage of generated messages that were delivered; (ii) message transmission overhead, the average number of transmissions required to deliver a message copy; and (iii) average energy consumption, the average amount of initial energy expended at the end of the simulation. 10 trials with different random seeds are simulated for each result in order to present the average and the 95% confidence interval.
## 3 Methodology
### Multilinear Regression Model  
Meteorological variables selected in Section 3.1 were used as input variables for both MLR and ANN models. Four MLR models were developed: one for all skies and three additional specific models for the clear, intermediate, and overcast sky types. To develop the MLR models, the data were divided into two groups: the first group, comprising 85% of the data, was used for model fitting, and the remaining 15% of the data was used for model validation. Conventional statistics were employed to evaluate the adequacy of fit for each model: coefficient of determination (R\({}^{2}\)), normalized root mean square error (nRMSE) and normalized mean bias error (nMBE), calculated by Equations (2)-(4), respectively.  
\[\mathrm{R}^{2}=\frac{\sum_{i=1}^{\mathrm{n}}\big{(}\mathrm{GHUVE}_{\mathrm{ mod}}-\overline{\mathrm{GHUVE}}_{\mathrm{mod}}\big{)}\cdot\big{(}\mathrm{GHUVE}_{ \mathrm{exp}}-\overline{\mathrm{GHUVE}}_{\mathrm{exp}}\big{)}}{\sqrt{\sum_{i=1} ^{\mathrm{n}}\big{[}\big{(}\mathrm{GHUVE}_{\mathrm{mod}}-\overline{\mathrm{ GHUVE}}_{\mathrm{mod}}\big{)}^{2}\cdot\big{(}\mathrm{GHUVE}_{\mathrm{exp}}- \overline{\mathrm{GHUVE}}_{\mathrm{exp}}\big{)}^{2}\big{]}}} \tag{2}\]  
\[\mathrm{nRMSE}=\frac{1}{\overline{\mathrm{GHUVE}}_{\mathrm{exp}}}\sqrt{\frac{ \sum\limits_{i=1}^{\mathrm{n}}\big{(}\mathrm{GHUVE}_{\mathrm{mod}}-\mathrm{ GHUVE}_{\mathrm{exp}}\big{)}^{2}}{\mathrm{n}}}\mathrm{100(\%)} \tag{3}\]  
\[\mathrm{nMBE}=\frac{1}{\overline{\mathrm{GHUVE}}_{\mathrm{exp}}}\frac{\sum \limits_{i=1}^{\mathrm{n}}\big{(}\mathrm{GHUVE}_{\mathrm{mod}}-\mathrm{GHUVE}_{ \mathrm{exp}}\big{)}}{\mathrm{n}}\mathrm{100(\%)} \tag{4}\]  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & & \(|\mathrm{r(GHUVE,Variable)}|\). & & & \\ \hline
**CIE Sky Type** & **[1–0.9]** & **(0.9–0.7)** & **(0.7–0.5]** & **(0.5–0.3]** & **(0.3,0]** \\ \hline All-sky & GHI & \(\theta_{z}\) & k\({}_{\mathrm{r}}\), D, \(\varepsilon\), RH, DNI, T & k\({}_{\mathrm{d}}\), DHI & \(\Delta\), WS, \(\psi\), CC \\ Overcast & GHI & \(\theta_{z}\), k\({}_{\mathrm{d}}\), DHI & k\({}_{\mathrm{r}}\), D, DNI, \(\Delta\), & \(\varepsilon\), RH, T & WS, \(\psi\), CC \\ Intermediate & - & GHI, \(\theta_{z}\) & k\({}_{\mathrm{t}}\), k\({}_{\mathrm{d}}\), D, DHI, DNI & \(\varepsilon\), RH, T & \(\Delta\), WS, \(\psi\), CC \\ Clear & GHI, \(\theta_{z}\) & - & T & k\({}_{\mathrm{t}}\), k\({}_{\mathrm{d}}\), D, \(\varepsilon\), RH, DNI & \(\Delta\), DHI, WS, \(\psi\), CC \\ \hline \hline \end{tabular}
\end{table}
Table 3: [PERSON]’s coefficients were calculated for the different variables.  
where n represents the number of experimental data points used for model fitting and testing in each case; \(\rm GHUVE_{exp}\) is the experimental value of \(\rm GHUVE\), and \(\rm GHUVE_{mod}\) is the modeled \(\rm GHUVE\) value.  
The mathematical expressions of the four regression models and the goodness of fit of each one are shown in Table 4. The model's fitting results presented a high determination coefficient with the experimental data (R\({}^{2}>0.92\)); however, the nRMSE values obtained through multilinear regressions exceeded 20% in all cases.
## 2 Materials and Methods
### Central Data Processing System  
The FRM4 DOAS project has achieved a significant milestone by developing the first central near real-time (NRT, 24 h latency) processing system for measurements from MAX-DOAS-type instruments. As indicated in previous sections, the system incorporates advanced retrieval algorithms for lower tropospheric and stratospheric NO\({}_{2}\) vertical profiles, lower tropospheric formaldehyde vertical profiles and total ozone columns.  
The system is implemented in an event-driven fashion using a succession of asynchronous modules combining bash scripts, Fortran programs and Python code. The execution of these modules is managed by independent entities. This modular design allows modules to function independently from each other and enables the seamless integration of new tools or updates. Event-driven in this context means that the arrival of a new file triggers the processing at any given stage through trigger lists (TLs). Currently, the system is designed to ingest spectra files that cover at most 1 local day of measurements. Except for the tropospheric module, all other modules need less than a minute to process a single daily file on a single processor. These modules are run on the BIRA-IASB compute servers. Due to the heavier computation for the tropospheric module, this module runs on a local super-computing facility (SPACEPOLE HPC, total available cores: 2688) and ensures that a typical daily file can be processed in a few minutes using up to 6 cores for a single file.  
Below, we first describe the general workflow of the processing system (Section 2.3.1), then the general concept of a module including the log- and history file production (Section 2.3.2); a short description of each separate module follows in Sections 2.3.3-2.3.7. Finally, the description of the submission of the level-2 GEOMS HDF4 files from the FRM4 DOAS database to the NDACC data handling facility and to the ESA Validation Data Centre (EVDC), as well as an overview of the current processing status, are given in Section 2.3.8.
# Locating the Parameters of RBF Networks Using a Hybrid Particle Swarm Optimization Method
## 2 Method Description  
The training error of the RBF network is expressed as:  
\[E(y(x,g))=\sum_{i=1}^{m}(y(x_{i},g)-t_{i})^{2} \tag{3}\]  
The value \(m\) stands for the number of patterns, and the values \(t_{i}\) denote the real output for the input. The vector \(g\) denotes the set of parameters of the RBF network. Usually, RBF networks are trained through a two-phase procedure:  
1. In the first phase, the \(k\) centers, as well as the associated variances are calculated through the K-means algorithm [50]. A typical formulation of the K-means algorithm is outlined in Algorithm 1.
2. In the second phase, the weight vector \(\overrightarrow{w}=(w_{1},w_{2},\ldots,w_{k})\) is estimated by solving a linear system of equations: 1. **Set**\(W=w_{kj}\); 2. **Set**\(\Phi=\phi_{j}(x_{i})\); 3. **Set**\(T=\{t_{i}=f(x_{i}),i=1,..,M\}\); 4. The system to be solved is identified as: \[\Phi^{T}\Big{(}T-\Phi W^{T}\Big{)}=0\] (4) with the solution: \[W^{T}=\Big{(}\Phi^{T}\Phi\Big{)}^{-1}\Phi^{T}T=\Phi^{\dagger}T.\] (5)  
The proposed work used two computational phases to optimally calculate the network parameters. Firstly, a promising range of the parameters of the network was calculated through an optimization process that incorporated interval arithmetic. Subsequently, the parameters of the network were trained with the usage of a genetic algorithm inside the located range of the first phase. The following subsections analyze both of these phases in detail.  
1. **Repeat**. 1. **For** each center \(c_{j}\), \(j=1..k\). 2. **For** each pattern \(x_{i}\), \(i=1,..,m\), **do**: 1. **Calculate**\(j^{*}=\min_{i=1}^{k}\big{\{}D\big{(}x_{i},c_{j}\big{)}\big{\}}\). 2. **Update**\(S_{j^{*}}=S_{j^{*}}\cup\{x_{i}\}\). 3. **EndFor**. 4. **For** each center \(c_{j}\), \(j=1..k\), **do**: 1. **Define**\(M_{j}\) as the number of points in \(S_{j}\). 2. **Calculate**\(c_{j}\): \[c_{j}=\frac{1}{M_{j}}\sum_{i=1}^{M_{j}}x_{i}\] 3. **EndFor**. 2. **Compute** the variances for every center as \[\sigma_{j}^{2}=\frac{\sum_{i=1}^{M_{j}}\big{(}x_{i}-c_{j}\big{)}^{2}}{M_{j}}\] 3. **Terminate** if there is no change in centers \(c_{j}\).
## 2 Related Work
When the program was running, the hardware-monitoring module would calculate the DI label and the CFI label of the currently executed BB. If the DI label or the CFI label was not the expected value, it was considered that the DI or CFI had been destroyed. However, this method did not consider indirect jumps. We previously tried to combine the CFI protection mechanism with the DI protection mechanism to not only monitor any tampering with the key data, but also to discover the jump address modified by the attacker [12]. However, this approach led to high system-performance overhead. Different from existing security studies that only protect data or the control flow of embedded systems, we propose a new architecture that can monitor the data integrity and control flow integrity of the execution program in real time. The architecture extracts the basic blocks of the program at the compiling time and calculates the static data digest. At the same time, the architecture allocates CFI labels to BBs containing direct jump instructions, and builds bitmaps of legal target addresses based on the type of indirect jump instructions. The HMM will calculate the dynamic data digest and compare it with the static digest to complete data integrity verification. For CFI verification, the HMM will calculate the Hamming distance (HD) between the CFI labels of BBs and compare it with the initial design value. In addition, for indirect jump instructions, the HMM will search for the value of the corresponding point of the target address in the bitmap to determine whether the jump is legal.
## 2 Prototype Bridge and the Scaled Model
### Scaled Model
#### 2.2.3. Girder and Stayed Cables  
In the prototype bridge, the deck is flat, thin walled steel box with many U shape shear key. It would be very difficult to manufacture the deck if the cross-section were scaled in ratio of 1/100 stickily. Therefore, it is simply designed according similitude ratios of bending stiffness in Table 2.  
The aluminum is selected as the material of girder for the characteristics of low elastics modulus, simply processed and installed. Figure 4 shows the size, area and moment of inertia of the cross section. The section stiffness \(EI_{\mathrm{z}}\) and \(EI_{\mathrm{z}}\) are calculated as 4.72e-06 m\({}^{4}\) and 1.50e-07 m\({}^{4}\). The actual bending stiffness ratios of \(EI_{\mathrm{z}}\) and \(EI_{\mathrm{z}}\) are 6.91E+08 and 5.00E+08. They are about 1.86 times and 2.56 times of target similitude ratios of bending stiffness in the Table 2.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \begin{tabular}{c} **Physical** \\ **Properties** \\ \end{tabular} & **1/Ratio** & \
\begin{tabular}{c} **Material** \\ **Properties** \\ \end{tabular} & **1/Ratio** &
\begin{tabular}{c} **Dynamic** \\ **Parameter** \\ \end{tabular} & **1/Ratio** \\ \hline Length & 1/102 & Elastic Modulus & 1/12.81 & time & 0.0707 \\ Area & 1/104 & bending Stiffness & 1/1.281 \(\times\) 109 & Frequency & 1/0.0707 \\ Moment of inertia & 1/108 & Equivalent density & 1/0.2562 & Velocity & 1/7.07 \\ Strain & 1 & Mass & 1/256,200 & Acceleration & 2 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Similitude ratios of the scale bridge model.  
Figure 3: Detailed design of testing specimen (**a**) towers and (**b**) piers.  
The high-strength steel wire was used to fabricate stayed cables. The diameters of cables were calculated strictly followed similitude ratio of axial stiffness in Table 2. In the model, diameters of the steel wires are 0.4 mm for C1-C8, 0.6 mm for C9-C14 and 0.9 mm for C15-C21. Material tests of the polymethyl methacrylate (PMMA) and other materials were conducted, and the average properties are shown in Table 3.  
#0, #1, #2 piers and #3 tower were placed on the middle-table (table 1#), and #4 tower, #5, #6 and #7 piers were placed on the right-side table (table 2#). All the towers and piers were fixed on the tables by high stress bolts.  
Figure 5c shows the elaborate roll bearings installed between the tower and girder. The bearings can roll freely in longitudinal direction, but is restrained in the lateral direction.
# Exploring Vitamin D Synthesis and Function in Cardiovascular Health: A Narrative Review
## 6 The Impact of Processing on Vitamin D Levels in Foods and Mitigation Strategies
### Impact of Processing on Vitamin D Levels
#### 6.1.2 Baking and Cooking  
Processing methods such as baking, cooking, frying, and water boiling induce a significant reduction in Vitamin D, although the extent of this loss varies depending on the type of food being cooked and the specific methods of heat application. [PERSON] et al. report that during heat treatment in an oven for 40 min at typical cooking temperatures, eggs and margarine retained about 39-45% of their Vitamin D content while frying preserved 82-84% of the vitamin, whereas boiling eggs achieved a retention rate of 86-88%. In the case of baked bread, rye bread retained 69% of its Vitamin D3, which was less than the 85% retention observed in wheat bread. Vitamin D2 retention was, instead, somewhat better, at 73% in rye bread and 89% in wheat bread [107]. Another study by [PERSON] et al. [108] demonstrated that both storage and cooking practices influence Vitamin D metabolites, with certain cooking methods showing true retention rates for Vitamin D3 ranging from 50-152%. The findings suggest that 25-hydroxy Vitamin D3-enriched eggs can serve as a potent dietary source of Vitamin D, with their total Vitamin D activity being significantly higher in comparison to non-enriched eggs depending on the storage and cooking method used.