esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Laboratory Test Verification  
Based on the above analysis results, to further verify the accuracy of the calculations and analysis, and the effectiveness of the finite element simulation, the laboratory test data are used to compare the simulation results of the displacement and stress of the aircraft wheel load acting on the pavement slab [49; 50; 51; 52]. When the aircraft's wheel load acts on the pavement slab, in the laboratory test, the displacement and stress placed on the slab, and its corresponding changes, are shown in Figures 13 and 14, respectively.  
As can be seen from Figures 13 and 14, when the aircraft's wheel load acts on the pavement slab, the laboratory test data are approximately consistent with the finite element simulation results, which is expected; however, there are some differences between the laboratory test data and the calculation results of the finite element model, which may be due to the following reasons:  
* the results are different due to the different conditions in laboratory test facilities and human errors;
* the results do not consider the stress and deformation caused by temperature changes on the pavement slab.  
Figure 12: Comparison of stress states caused by the aircraft’s wheel load acting on different positions of the pavement slab.  
Therefore, future research will continue to carry out investigations into the influence of the aircraft's wheel load, coupled with temperature, on the laying of prefabricated runway slabs. Moreover, future research will also continue to verify these results, as well as the engineering applications of the finite element simulation results from laboratory tests and full-scale tests.  
Figure 14: Comparison between the laboratory test and simulation results [49; 50; 51; 52].  
Figure 13: Laboratory test [49; 50; 51; 52].
## 3 Materials and Methodology
### Applying CCDF to Assess Transportation Network Performance  
To systematically characterize the network performance changes under critical-link attacks, this study evaluated the empirical distribution functions of each topological parameter. Since the main focus of contemporary risk management studies is on the tail of the distribution function, this study utilized the CCDF, which is often used in reliability engineering to understand the likelihood of extreme events. The CCDF is particularly effective when dealing with heavy-tailed distributions or rare occurrences, making it an essential method for analyzing scenarios where critical disruptions significantly impact the network.  
Mathematically, the \(CCDF\) defines the probability that the random variable \(X\) is greater than or equal to a particular value \(x\), which is represented as \(P(X\geq x)\). Consequently, related to the cumulative distribution function, the CCDF can be expressed as follows:  
\[CCDF_{(X)}=1-CDF_{(X)} \tag{5}\]  
where \(CCDF_{(X)}\) is the probability that \(X\) is less than or equal to \(x\).  
One of the strengths of the CCDF is its ability to visualize and interpret the likelihood of extreme values in a way that highlights the critical points of failure in a system. Unlike traditional cumulative distribution functions (CDFs), which focus on the probability of values below a threshold, the CCDF emphasizes the upper tail of the distribution, where extreme or rare events reside. This makes it particularly well suited for analyzing heavy-tailed distributions, such as those often observed in network centrality metrics under stress conditions like network disruption. By focusing on the tail, the CCDF reveals the relative importance of critical nodes and edges, which are often obscured in traditional CDFs or other methods.  
In the context of this study, the CCDF was instrumental in identifying the segments of the transportation network most susceptible to critical-link attacks. For instance, as the CCDF curves drop steeply at the tail under attack scenarios, it signals a significant loss of flow capacity and highlights the network's reduced ability to redistribute traffic effectively. The CCDF also enables clear comparisons between different scenarios, revealing how the importance of critical components changes under varying levels of stress. This underscores the CCDF's unique ability to capture the heavy-tailed nature of centrality distributions under stress, providing a more comprehensive understanding of network resilience.  
By leveraging the CCDF, this study not only quantified the impacts of critical link disruptions but also revealed patterns of vulnerability that would have been less evident using traditional methods. These insights are vital for formulating strategies to enhance network resilience, such as reinforcing critical links, improving redundancy, and prioritizing maintenance in segments with higher probabilities of extreme events.
# Review of V2X-IoT Standards and Frameworks for ITS Applications
## 2 Core ITS Standards  
As was mentioned before, ITS not only includes vehicles and their drivers, but also passengers, pedestrians, traffic management specialists, roadside infrastructure, etc. All of these ITS components must be interlinked in order to exchange information and/or retrieve data from the surrounding environment. However, such an ITS system can successfully exchange data only if it is developed in accordance with international standards. At first glance, it may appear simple to identify the standards that describe ITS systems as the International Organization for Standardization (ISO) has a special ITS Committee (ISO/TC 204) [22]. However, the situation is much more complicated because both regional standards, such as the European Telecommunications Standards Institute (ETSI) and the European Committee for Standardization (CEN) standards, as well as national standards, such as those of the Association of Radio Industries and Businesses (ARIB) in Japan, and international ones, must be taken into account. Standards, either national, regional or international, have to operate within a framework of regulations. These are mainly national regulations, managed by such commissions as the United States Federal Communications Commission (FCC), or the Australian Communications and Media Authority (ACMA). The regulations are also complied within the European Union countries, but in order to develop a common market, the European Conference of Postal and Telecommunications Administrations (CEPT) have developed recommendations for the most commonly used ITS frequency bands among the European Union (EU) countries and other CEPT members. At the international level, similar recommendations are also made by the International Telecommunication Union (ITU) (through its ITU Telecommunication and ITU-Radio divisions), whose main objective is to promote global interoperability of standards.  
The standards for DSRC, C-V2X technologies and their regulated frequency spectrum bands are discussed in this subsection.
## 4 Results
### Homogenisation Macrostructural Results  
#### 4.2.1 Homogenisation  
The model of the microstructure of the cortical bone tissue (see Figure 4), was used to solve the micromechanics equations presented in Section 3.2, under the finite element framework, with the objective of obtaining the homogenised mechanical properties tensor (Equation (10)) from the micromechanical analysis of the microconstituents of the microstructure.  
The microstructure of the cortical bone was considered to be composed of mineral bone material and pores (voids). Mineral bone is considered at the observation scale of our model as a homogeneous material with orthotropic (2D) mechanical behaviour along the longitudinal and transversal directions, according to the literature [58; 59]. After a fitting iterative procedure of the experimental results (introduced and explained below), the following values were obtained for the mechanical behaviour of bone mineral tissue: \(\mathrm{E_{L}}=20\) GPa, \(\mathrm{E_{T}}=48\) GPa, \(\mathrm{G_{LT}}=12.5\) GPa and \(\mathrm{v_{LT}}=0.395\). These mechanical properties are associated to the following orthotropic tensor:  
\[\mathbb{C}^{p}=\left[\begin{array}{ccc}77.0&30.4&0\\ 30.4&32.0&0\\ 0&0&12.5\end{array}\right][\mathrm{GPa}] \tag{14}\]  
Therefore, after the homogenisation procedure, the macroscopic (or apparent) mechanical properties tensor yields to:  
\[\mathbb{C}^{0}=\left[\begin{array}{ccc}23.13&8.63&1.01\\ 8.63&19.54&0.91\\ 1.01&0.91&6.90\end{array}\right][\mathrm{GPa}] \tag{15}\]
# Nanomaterials in Photocatalysis: An In-Depth Analysis of Their Role in Enhancing Indoor Air Quality
## 7 Other Metal-Oxide Nanomaterials  
Other than the most common titania-based and carbon-based nanomaterials, various materials such as metal oxides have been used in photocatalysis. Research about the least common nanomaterials especially ZnO [184] and WO\({}_{3}\)[185] have increased in recent years.
# Logistics Service Provider Lifecycle Model in Industry 4.0: A Review
## 4 Concept of Logistic Service Provider Lifecycle Model (LSLM)
### The End of Life (EOL) Phase
#### 4.3.2 Evaluation of Customer Satisfaction  
The evaluation of customer satisfaction is very important for the logistics lifecycle in a new market economy [105]. First, sales trends are modified to analyze market trend strategies as to whether customer targets can be maintained or not. In addition, while sales trends can predict sales forecasts and demand marketing, they cannot know customer satisfaction or respond to customer needs [68; 77]. Then, the evaluation is reviewed to offer service requirements to improve customer acceptance. Therefore, the factor of evaluation of the service lifecycle reviews the service lifecycle and another lifecycle management model such as product lifecycle management, product-service system, and close-loop management [15; 101; 119].  
Sub-Criteria of Evaluation of the Service Lifecycle  
The evaluation of the service lifecycle is an excellent factor for improving and satisfying customers to maintain customer trust and loyalty to a company [73; 82]. Service applies through every stage in logistics operations, such as an idea, design, logistics operation, cross-docking, warehouse management, maintenance, and disposal [73; 82]. Full service in all lifecycles is a substantial factor in logistics management. Lifecycle management has four separate parts, namely, introduction, growth, maturity, and decline state [21; 22; 120].  
Sub-Criteria of Improvement and Development or Re-Design/Re-Thinking of Product and Service  
Re-design and re-thinking are adapted to respond to customer requirements in this situation to rapidly change and extend products and services throughout a new lifecycle [68]. The evaluation criterion is a factor to improve or develop a product and service to re-design logistics lifecycle management [86]. Product Service Systems and Product Lifecycle Management effectively improve manufacturer operations to implement a closed-loop lifecycle and satisfy customer requirements [17; 67]. Thus, improving information technology is increasing for supporting logistics operations in the supply chain, and maintaining customer responses [30; 75; 76]. A cloud system is adopted to implement the efficiency of logistics operations, which reduces cost, provides faster sharing of tangible/intangible information, and improves customer trust and loyalty in the e-commerce economy [26].
###### Abstract  
This study presents a new adaptive trajectory tracking control scheme for a fully actuated Unmanned Surface Vehicle (USV) to track a common moving target region. In this control concept, the desired objective trajectory is specified as a moving region instead of a moving point, and so which is called non-strict trajectory tracking. Within this control scheme, a regression matrix is used to handle the parameter uncertainties, and region-based control scheme is used to track a desired moving region. A switching gain control term based on the exponential function is proposed to make the USV's trajectory converge into the desired moving region rather than converge on the boundary of the moving region, and to reduce system chattering at the same time. A Lyapunov-like function is presented for stability analysis of the proposed control scheme. Numerical simulations are conducted to demonstrate the performance of the proposed non-strict trajectory tracking control scheme of the USV.  
unmanned surface vehicle (USV); trajectory tracking; region control; adaptive control Article  
Adaptive Non-Strict Trajectory Tracking Control Scheme for a Fully Actuated Unmanned Surface Vehicle
# A Precise and Stable Space-Based Time System for Navigation in Smart Cities
## 4 Design of the Space-Based Time System for Navigation Constellations
### Space-Based Integrated Atomic Time Algorithm Based on ALGOS Algorithm  
From Equation (28), it is evident that the inter-satellite clock offset reflects the relative clock offset of two satellites, and its noise encompasses the noise of the two clocks. The ALGOS method is employed to construct the inter-satellite integrated atomic time, enabling a more accurate evaluation of the space-borne atomic clock noise.  
The ALGOS algorithm, initially utilized by the International Bureau for Weights and Measures (BIPM) [20], generates the international reference Coordinated Universal Time (UTC) on a monthly basis. The UTC calculation using ALGOS involves three steps:  
1. The Echelle Atomique Libre (EAL) is calculated as a weighted average of 350 free-running atomic clocks, optimizing the long-term frequency stability through a specially designed weighting method;
2. The frequency of EAL maintains agreement with the International System of Units (SI) second, providing the International Atomic Time (TAI); the comparison between the EAL frequency and the primary frequency standards yields the steering correction;3. Coordinated Universal Time (UTC) is finally calculated, incorporating leap seconds to maintain agreement with the time derived from the rotation of the Earth.  
To implement the ALGOS method, determining the weights of different space-borne atomic clocks is necessary. Using a star with high stability as a reference (as introduced in Section 4.3), the clock offset of each satellite relative to a certain satellite is estimated. The acquired clock offset sequence is then regressed long-term to deduce the clock drift and speed. The top-N space-borne atomic clocks with the highest stability are selected, and comprehensive atomic time is constructed in an equal-weighted manner:  
\[TA1(t_{0})-clk_{A}(t_{0})=\frac{\sum\limits_{i=1}^{N}CLK_{iA}^{0}(t_{0})}{N}, \tag{30}\]  
where \(N\) is the number of satellites when constructing the synthetic atom, and \(i\) is the index of the satellite. Although the absolute value of the preliminary integrated atomic time \(TA1(t_{0})\) cannot be obtained, the clock offset of the reference star relative to the integrated atomic time can be obtained by Equation (30). Subtracting Equations (27) and (30), the clock offset of each star relative to the integrated atomic time can be obtained as follows:  
\[TA1(t_{0})-clk_{B}(t_{0})=TA1(t_{0})-clk_{A}(t_{0})-(clk_{B}(t_{0})-clk_{A} (t_{0})). \tag{31}\]  
Finally, the space-based atomic time is output as  
\[TA1(t_{0})-clk_{A}(t_{0})=\frac{\sum\limits_{i=1}^{N}\omega_{i}CLK_{iA}^{0}( t_{0})}{N}, \tag{32}\]  
where \(\omega_{i}(t)\) is determined based on the Allan variance of the space-borne atomic clock offset.
## 3 Network Dynamic Evolution and Empirical Analysis
### The Model on the DQF
#### 3.2.2 Model Calibration and Empirical Analysis  
The MFD on the DQF in Figure 6 is also calibrated by empirical data. For subregion 2, \(k_{2c}=34.17\) veh/km/lane, and the scatters on the MFD of subregion 1 and 3 are distributed within the critical density, then \(k_{1c}=30\) veh/km/lane, \(k_{3c}=30\) veh/km/lane, \(q_{1c}=364\) veh/h/lane, \(q_{2c}=450\) veh/h/lane, and \(q_{3c}=328\) veh/h/lane. The calibration results of the transfer ratios, the external inflow ratio, and the outflow ratio are listed in Table 3.  
In simulation, the initial densities on the DQF are (4,3,2) veh/km/lane at 7:00, and the evolutions of the system over time are shown in Figure 12. Figure 12a is the evolutions of subregions 1 and 2, and Figure 12b is that of subregions 2 and 3. The blue line is the model simulation result, and the scatters are the empirical data. Points A and B represent the initial state and final state, respectively. Since the traffic flow is localized to the west of the urban network during the morning peak, the transfer ratio \(P_{32}\) is significantly higher than those of other subregions. This leads to the network density of subregion 2 quickly exceeding the critical density and evolving toward congestion. The network states in subregions 1 and 3 are in free-flow state, and the model simulation results are consistent with the ground truth.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Model Parameters** & \(P_{12}\) & \(P_{21}\) & \(P_{23}\) & \(P_{32}\) & \(P_{1A}\) & \(P_{1E}\) & \(P_{2A}\) & \(P_{2E}\) & \(P_{3A}\) & \(P_{3E}\) \\ \hline DQF & 0.3 & 0.28 & 0.2 & 0.6 & 0.08 & 0.1 & 0.2 & 0.18 & 0.16 & 0.15 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Parameters calibration of the model on the day of Qingming Festival.
## 2 Materials and Methods
### Clustering  
The number of decomposed FID components is larger than the number of metabolites in the FID signal. Hence, FID components need to be clustered into the number of interest metabolites. Initialized K-means is utilized to determine the cluster of components corresponding to each metabolite. K-means clustering partition number of observations; here are spectrum of the FID components, into number of clusters; here are number of metabolites, in which each component belongs to the metabolite with the nearest mean. To this end, first, the components are divided into three groups that are based on their maximum peak location in the parts per million (ppm) domain. The first group has the metabolites with peaks locations below 2.15 ppm, the second group between 2.16 and 3.85 ppm, and the third group has peaks above 3.86 ppm. Second, in each group, clusters centroids are initialized while using prior knowledge of each metabolite chemical structure, and their peak locations. Table 1 details the initialized cluster centroids, as acquired from [29]. Figure 3 shows the clustering result. The clustering process is done subject-wise, meaning that we do not learn cluster centroids from the  
Figure 2: Spectra of an in vivo free induction decay (FID) signal components decomposed with Hankel singular value decomposition (HSVD).  
dataset, and they are already learnt. A clustering process for one of the subjects (basis sets spectra is shown in the Figure 2) is demonstrated, step-by-step, with figures in the Appendix A.  
Some of the metabolites (i.e., NAA, Creatine, Myo-inositol, and GABA) employ two cluster centroids, as shown in Table 1. Creatine, Myo-inositol, and GABA have two strong peaks that are well removed from each other and, therefore, two cluster centers for each of them were used. NAA has one large peak and one small peak. If the small peak is neglected, then the components of that peak will be picked up by other cluster centroids, and the error in calculating the metabolites concentrations of those clusters increases.
## 3 Case Study
### The Components and Photovoltaic Output Prediction  
After obtaining the feature set of each component, each component is input into the DBN along with its corresponding feature set to conduct the prediction. When training the DBN network, a 3-input 1-output model is used, that is, three points of each component and three points of the corresponding features are input together. The number of hidden layers in the parameter setting process and the number of units in each hidden layer references to [35], and other parameters setting uses reference [36]. Then, the DBN structure of each component is as shown in Table 4.  
After obtaining the structure of each component prediction model, components with various frequencies are predicted using the model trained well, and the prediction result is shown in Figure 7.  
Combining Figure 7 and the corresponding RMSE error in Table 2, it can be seen that from IMF1 to IMF10, as the component fluctuations weaken, the error begins to decrease, especially after IMF7, the error is relatively small. But overall, the prediction results of each component are ideal.  
After the prediction of each component is completed, the DBN is used to integrate the prediction results of each component. Taking the actual value of each component as input and taking the actual photovoltaic output value as output fortraining the model, the prediction values of the components are taken as inputs, and using the model trained well to obtain the photovoltaic prediction values. The prediction results are shown in Figure 8.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Component** & **Structure** \\ \hline IMF1 & 3-24-12-1 \\ IMF2 & 3-16-6-4-1 \\ IMF3 & 3-20-6-1 \\ IMF4 & 3-20-8-1 \\ IMF5 & 3-16-8-1 \\ IMF6 & 3-24-6-1 \\ IMF7 & 3-16-4-1 \\ IMF8 & 3-12-6-1 \\ IMF9 & 3-12-8-1 \\ IMF10 & 3-8-1 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The DBN structure of each component.  
Figure 7: _Cont._  
Figure 7: The prediction image of components with various frequency.  
The left half of Figure 8 is the result plot of the prediction and actual values, and the right half is the comparison of the actual value with the prediction value; the green point in the figure is the distribution point of the prediction value. It can be seen from Figure 8 that compared with the error of other intervals, except for the interval 35-40 has the larger error; in the other intervals, the actual value and the prediction value are very close, and most of the green points distribute near the actual value.