esa-sceva/satcom-chunk-collection huggingface dataset:
## Improving Localization Accuracy: Successive Measurements Error Modeling  
**[PERSON] 1,* and [PERSON] 2**  
* College of Information Technology, United Arab Emirates University, Al-Ain 15551, Abu Dhabi
* Faculty of Computer and Information Sciences, Mansoura University, Mansoura 35516, Egypt; E-Mail: [EMAIL_ADDRESS]
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +971-3-7135543.  
Academic Editor: [PERSON]  
_Received: 4 March 2015 / Accepted: 19 June 2015 / Published: 1 July 2015_  
**Abstract:** Vehicle self-localization is an essential requirement for many of the safety applications envisioned for vehicular networks. The mathematical models used in current vehicular localization schemes focus on modeling the localization error itself, and overlook the potential correlation between successive localization measurement errors. In this paper, we first investigate the existence of correlation between successive positioning measurements, and then incorporate this correlation into the modeling positioning error. We use the Yule Walker equations to determine the degree of correlation between a vehicle's future position and its past positions, and then propose a \(p\)-order Gauss-Markov model to predict the future position of a vehicle from its past \(p\) positions. We investigate the existence of correlation for two datasets representing the mobility traces of two vehicles over a period of time. We prove the existence of correlation between successive measurements in the two datasets, and show that the time correlation between measurements can have a value up to four minutes. Through simulations, we validate the robustness of our model and show that it is possible to use the first-order Gauss-Markov model, which has the least complexity, and still maintain an accurate estimation of a vehicle's future location over time using only its current position. Our model can assist in providing better modeling of positioning errors and can be used as a prediction tool to improve the performance of classical localization algorithms such as the Kalman filter.
## 3 Results and Discussion
### Biological Responses
The DNA strand break values and the LPO results are shown in Figure 2. In the present study, the DNA strand breaks exhibited significantly higher values (\(p\) < 0.05) than T0 in the gills of _Perna perna_ mussels exposed to a pH value of 6.0 in both exposure times. Only LPO was significantly different (\(p\) < 0.01) at pH 6.0 after 96 h exposure. Oxidative stress occurs due to an imbalance between the production of reactive oxygen species (ROS) and their elimination via the antioxidant system defense, leading to protein degradation and enzyme inactivation [94]. If it is continuously generated, ROS can damage important biomolecules such as DNA, proteins and LPO [95]. Results obtained from this study may be related to the increase in the ROS production in acidified scenarios, agreeing with the study [96], where bivalve _Crassostrea gigas_ showed a significant increase in the production of ROS in hemocytes, as well as DNA damage (comet assay) in different scenarios of ocean acidification (pH 7.6 and 7.8). Ref. [97] reported that low pH caused a decrease in electron supply from NADH to the electron transport chain in the oyster _Crassostrea gigas_, and their response mechanism changed the cellular balance between resource supply and oxidative stress. As a protective system against the consequences of oxidative stress, lysosome autophagy consists  
Figure 2: Mean (and standard error) of DNA damage (stand break) and LPO assessed in _P. perna_ exposed to different pH levels for 96 h (\({}^{**}\)\(p\) < 0.01). of breaking down longer-lived proteins and organelles and recycling the products into protein-synthesis and energy production pathways [98].
## 5 Results and Discussion
### Discussion  
Overall, the results show that our MME Framework outperforms SME for our case studies. In particular, the MME Framework with majority vote as an ensemble strategy for the three different combinations of factors--(i) noise level, (ii) feature informative level, and (iii) number of features--has achieved the best results. The robustness of our framework compared with SMEs becomes even more evident as the number of features and the number of informative features increases. The results also reveal that the noise in the data does not affect the final feature importance estimates, as there are no significant changes as the noise increases.  
One advantage of using our model-agnostic MME Framework is that it avoids the worst-case scenario for feature importance estimates due to the inherent advantage of ensembles being more robust against spread and dispersion of the predictions. Additionally, using only one set of explainability can potentially bias the feature importance estimates. Similarly, the feature importance estimates might be biased if only a single method is employed as it might not consider the synergy between orthogonal features. By fusing feature importance estimates of multiple ML models and methods, we can achieve a final feature importance estimates that is more robust. For safety-critical systems, it is important for the feature importance estimates to be robust to noise, biases, and anomalies for end-users to explain the results of ML models accurately.  
Conversely, this can also be a disadvantage as the best performing feature importance estimate might be moderated by worse methods. However, in real-world, safety-critical systems where the ground truth is unknown, it is important to rely on not only a single method but also the potential bias it might produce. In scenarios where all feature importance determined by different methods disagree or there is no consensus on feature importance, RATE or majority vote is reduced to the average of all feature importance vectors. Under such circumstances, the best option for safety is to further investigate the reasons for disagreement between ML model or feature importance techniques. Furthermore, while the majority vote achieves the best results for many different data characteristics in this paper, it is not always the case. Further studies are necessary to decide which ensemble method is definitely better for specific data characteristics.  
For our experiments, we keep the hyperparameters of each ML model constant as their case-based optimisation is likely to affect feature importance estimates for different data characteristics. In the future, it is necessary to further investigate the interplay between parameter optimisation and feature importance. In addition, other factors such as covariate drift on the test dataset and imbalanced datasets should be included in the framework tests.
## 2 Theoretical background  
### Classical Laminated Plate Theory  
A multi-degree of freedom analytical model [1; 2; 37] was introduced in order to study the impact event. A simply supported plate was analytically solved by using the Classical Laminated Plate Theory. The solutions for the analytical approach were obtained in terms of contact force, maximum displacement, impactor velocity, and kinetic energy. Then, a system of ordinary differential equations (ODE), representing the simply supported plate, coupled with a differential equation representing the impactor motion, was obtained.  
A laminated composite plate, with in-plane dimensions \(a\) and \(b\), respectively, along the x- and y-directions, was considered. The plate was considered simply supported along the four edges, and a concentrated force was applied at the center (x\({}_{0}\) = a/2, y\({}_{0}\) = b/2). Under these assumptions, the equation of motion along the transverse direction can be expressed as the following system of ordinary differential equations [1]:  
\[\ddot{a}_{mn}+\omega_{mn}^{2}\alpha_{mn}=\frac{4F}{abI_{1}}\sin\left(\frac{m \pi x_{0}}{a}\right)\sin\left(\frac{n\pi y_{0}}{b}\right), \tag{1}\]  
In Equation (1), \(F\) is a concentrated applied force, while \(\omega_{mn}\), which is the natural frequency of the system for the strain modes \(m\),\(n\), is given by:  
\[\omega_{mn}=\sqrt{\frac{\pi^{4}}{a^{4}I_{1}}(D_{11}m^{4}+2(D_{12}+2D_{66})m^{ 2}n^{2}r^{2}-4D_{16}m^{3}nr-4D_{26}mn^{3}r^{3}+D_{22}n^{4}r^{4})}, \tag{2}\]  
where \(D_{ij}\) are the terms of the bending stiffness matrix, \(I_{1}\) is the mass per unit length, and \(r\) = \(\psi\).  
As reported in [1], when both values of \(m\) and \(n\) are negative, the effects of deflection in the plate become negligible. The choice of high positive values for both values is related to the phenomenon being analyzed: as the values of \(m\) and \(n\) increase, the rotary inertia and shear deformation effects becomes significant.  
Moreover, the following Equation (3), governing the impactor dynamic, has been considered:  
\[M\ddot{z}(t)+F_{c}(t)=0, \tag{3}\]  
where \(M\) and \(\ddot{z}(t)\) are, respectively, the impactor mass and acceleration, \(F_{c}(t)\) is the contact force, and \(t\) is the time variable.  
A proper contact law has been chosen to couple Equations (3) and (1). In particular, the contact force \(F_{c}(t)\) has been related to the indentation \(\alpha=z(t)-w_{0}(x_{0}\),\(y_{0}\),\(t)\), expressed as the difference between the impactor displacement \(z(t)\) and the laminate central point displacement \(w_{0}(x_{0}\),\(y_{0}\),\(t)\). In this work, a linearized form of the Hertz Contact Law was adopted [37], therefore the contact force history, exerted during the impact phenomenon, can be expressed as:  
\[F_{c}(t)=k_{y}\alpha, \tag{4}\]  
where \(k_{y}\) represents the linearized contact stiffness.  
The boundary conditions of the system of ODEs are:  
\[\alpha_{mn}(0)=\dot{\alpha}_{mn}(0)=0, \tag{5}\]  
\[\begin{array}{l}z(0)=0\\ \dot{z}(0)=V\end{array} \tag{6}\]  
where \(V\) is the initial velocity of the impactor.
# GNSS/INS Integration Based on Machine Learning LightGBM Model for Vehicle Navigation
## 2 Introduction to LightGBM Machine Learning Model Based on PUA  
### LightGBM Machine Learning Model  
Gradient boosting decision tree (GBDT) is a common machine learning model used in artificial intelligence that employs several basic learners that are iteratively trained to produce the optimal model. LightGBM is a distributed and efficient framework for implementing the gradient boosted decision tree (GBDT) algorithm [17], which fits a new decision tree by approximating the negative gradient of the loss function as the residual of the current decision tree.  
To improve the efficiency of machine learning model training while losing as little accuracy as possible in model prediction, the following seven optimization methods exist for LightGBM: (1) histogram-based decision tree algorithm, (2) gradient-based one-side sampling (GOSS), (3) exclusive feature bundling (EFB), (4) leaf-wise with max depth limitation, (5) categorical feature support, (6) parallel learning support, and (7) increase cache hit chance. Methods (1)-(4) are optimization algorithms, and (5)-(7) are optimizations for engineering applications.  
GOSS and EFB, two main techniques used by LightGBM, improve efficiency by reducing the number of samples and features, respectively. The GOSS algorithm retains all instances with large gradients and samples with small gradients at random. EFB bundles mutually exclusive features and can losslessly reduce feature dimensionality.
# Influence of Different Fibers on Performance of Bitumen Binders and Thin-Overlay Bitumen Mixtures
## 4 Conclusions  
In this paper, the performance of the fiber itself and the effect of the fiber on the crack resistance and toughening efficiency of SBS-modified bitumen and bitumen mixture were studied. The conclusions are as follows.  
1. The microscopic surface morphology of the three fibers is bundle-like and the interior is a solid structure. The oil absorption rate of polypropylene fiber is high, reaching 5.423. It shows that the dispersion and density of bundled fibers with similar morphology will have a significant impact on the oil absorption rate.
2. The addition of fiber can effectively improve the high-temperature rheological properties of SBS bitumen, but the elasticity at low temperatures is reduced. When the  
Figure 12: Toughness index.  
fiber content reaches 4%, the G*/sin\(\delta\) of SBS bitumen increases by about 107.04% on average, while \(S\) decreases by 89%. This means that the combination of fiber and free bitumen has a double-sided effect on the binder.
3. The incorporation of fiber significantly improves the crack resistance and toughness of bitumen mixtures, and the improvement effect of 4% polypropylene fiber is the most significant. According to the results of IDT, \(R_{\rm T}\) and \(T_{\rm FAC}\) of P-4 increased by 29.14% and 4.59%, respectively. The SCB test showed that the \(G_{f}\) and \(FI\) of P-4 increased by 78% and 84.32%, respectively. These findings underscore the importance of fiber dispersion in achieving a uniform distribution within the mixture, which subsequently influences the effectiveness of the fiber network structure and its bridging properties.  
In choosing fibers to enhance the mixture's crack resistance, it is necessary to consider the double-sided effect caused by the type and content of the fibers. In subsequent studies, we will explore the effects of more factors on the crack resistance of fiber mixtures and study the effects of these choices on practical applications.  
Conceptualization, J.W.; methodology, J.M.; investigation, Y.H. and P.L.; data curation, W.W. and C.Y.; writing--original draft preparation, J.M.; writing--review and editing, Y.H. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the National Key Research and Development Program of China (No. 2021 YFB2601000), a Guangxi Science and Technology Major Project (No. 2023 AA14005), and the Guangxi Key Research and Development Program (No. AB21220070).  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
Author [PERSON] was employed by the company Jiangxi Transportation Engineering Group Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
# Assessment of Carbon Emissions at the Logistics and Transportation Stage of Prefabricated Buildings
## 2 Research Boundary and Key Parameters
### Carbon Emission Measurement Methods  
Currently, the commonly used carbon emission measurement methods can be roughly divided into the following three types: the emission factor method [12], the mass balance method [13], and the actual measurement method [14] (see Table 1). The emission factor is the amount of carbon dioxide emitted per unit of product produced or energy consumed [15]. This method calculates carbon emissions by taking the average amount of CO\({}_{2}\) released and turning it into a carbon emission factor. This is usually available as a default value from the Intergovernmental Panel on Climate Change (IPCC). The formula for calculating carbon emissions is shown in Equation (1):  
\[\text{Carbon emissions}=\text{Carbon emission factor}\times\sum\text{activitydata}. \tag{1}\]  
Greenhouse gas emissions and energy consumption are measured near the carbon emission sources using professional testing equipment.  
Based on the analysis method of the law of conservation of mass, a model is made to statistically examine the substances that go into the research object and where they go when it is done.  
In summary, for this study, we adopted the emission factor method as the carbon emission measurement method for the transportation stage of assembled prefabricated  
\begin{table}
\begin{tabular}{c c c c} \hline
**Category** & **Advantages** & **Disadvantages** & **Applicable Scale** \\ \hline \multirow{4}{*}{**Emissions Factor method**} & 1. Easy to calculate & & \\  & 2. Authoritative database of & 1. Poor accuracy & \\  & carbon emission factors is available & 2. Large differences in carbon & \\  & 3. Not limited by & emission factor database & \\  & testing instruments & & \\ \hline \multirow{2}{*}{**Real Measurement method**} & 1. High precision & 1. High cost & \\  & 2. Data not easily available & \\ \hline \multirow{2}{*}{**Mass Equilibrium method**} & 1. Higher precision & & \\  & 2. Carbon emission sources & 1. High workload & \\  & are clear & 2. Data may not be representative & \\ \hline \end{tabular}
\end{table}
Table 1: Comparison of carbon emission measurement methods [13; 14; 15].  
buildings. Carbon emissions are a product of the energy consumption of a certain transportation means and the carbon emission factor of the energy consumed.
## 3 Methodology  
This research utilised a mixed methods approach combining secondary sources with primary data collected through a survey, semi-structured interviews, and expert roundtable discussions. This methodology enabled investigating the research questions from different perspectives and gathering insights from diverse experts across countries and sectors.  
The research design involved first conducting a review of the relevant literature to establish a base level of understanding of current state of the art in the field. Through an initial review of the background literature, including peer-reviewed academic articles, International Maritime Organization (IMO) guidelines, international laws, treaties, and case law, on the broad themes of maritime cybersecurity, maritime hybrid security, threats to shipping, and maritime security threats in Southeast Asia, as well as the grey literature, industry reports, media reporting, and relevant databases, the researcher identified key themes leading to the formulation of research questions. The research questions guided the formulation of survey and interview questions along several key themes including the  
Figure 1: Maritime Cyber Incidents by Year 2001–2023. Recreated by author based on data from Maritime Cyber Attack Database (MCAD) NHL Stenden University, Netherlands www. maritimcybersecurity.nl, accessed on 15 December 2023.  
following: the nature of technological developments in the maritime sector, the security implications of technology in the maritime sector, the nature and sufficiency of governance frameworks in relation to technological developments, and analysis of threat actors in the maritime sector. This led to formulation of three overarching key research questions for this study:  
RQ1: Taking account of technological advancements in ICT systems in ships, what are the greatest cyber and hybrid threats to maritime transportation systems, specifically to ships transiting congested waterways?  
RQ2: What threat actors are most likely to exploit cyber vulnerabilities in shipping and what attack vectors might they employ?  
RQ3: What existing international, national, regional or domestic governance regimes address maritime cybersecurity and where are the gaps or weaknesses?  
Secondary data included academic articles, international law texts such as the International Safety Management code (ISM), the Safety of Life at Sea convention (SOLAS), other international maritime treaties, case law, media reports, the grey literature, and data from the Maritime Cyber Attack Database (MCAD) compiled by NHL Stenden University of Applied Sciences, Netherlands.  
Primary data sources are detailed in the following sections.
## 3 Proposed Solution to Predict the AoA
### System Model and Mathematical Formulation
#### 3.3.2 Predictive Signal Model Under User Mobility
For example, the number of multipath components and their angular spread can be adjusted to simulate rich-scattering or corridor-type environments. In the broader beamforming context, Equation (1) serves as the foundation for constructing the received snapshot matrix \(X(t)\in\mathbb{C}^{N\times T}\) which aggregates signals across antennas and time. This matrix is used to compute the spatial covariance matrix \(R_{x}=\frac{1}{T}\sum_{t=1}^{T}X(t)X^{H}(t)\), which feeds into subspace-based AoA estimation algorithms such as MUSIC, enhanced MUSIC, or CW-P-MUSIC. The accuracy of these estimators and the efficacy of the beamforming weights derived from them depend critically on how well the signal model reflects true propagation characteristics. By simulating \(\theta_{\text{u}}(t)\) as a time-varying signal and augmenting it with multipath and fading effects, we create a rigorous testbed for predictive AoA estimation. The AoA traces produced by this model directly feed into our clustered sliding window regression pipeline, where adaptive LR and curvature-adaptive SVR are executed on the most relevant segment of each AoA history. This enables us to quantify improvements in estimation accuracy, spectrum sharpness, and beamforming gain when using models like LR, SVR, or the proposed DE-ACSW-R.
# A Data-Driven Model for Predictive Modeling of Vortex-Induced Vibrations of a Long-Span Bridge
## 3 Data Description
The remaining one, that is, the Pearson correlation coefficient (PCC), is employed to evaluate the sequence-to-sequence error. MAE, RMSE, WMAPE and PCC are calculated as follows:  
\[\text{MAE}=\frac{1}{N}\sum_{i=1}^{n}\lvert y_{i}-\hat{y}_{i}\rvert \tag{18}\]  
\[\text{RMSE}=\sqrt{\frac{1}{N}\sum_{i=1}^{N}\left(y_{i}-\hat{y}_{i}\right)^{2}} \tag{19}\]  
\[\text{WMAPE}=\sum_{i=1}^{N}\frac{\lvert y_{i}\rvert\lvert y_{i}-\hat{y}_{i} \rvert}{\sum_{i=1}^{N}\lvert y_{i}\rvert}=\frac{\sum_{i=1}^{N}\lvert y_{i}- \hat{y}_{i}\rvert}{\sum_{i=1}^{N}\lvert y_{i}\rvert} \tag{20}\]  
\[\text{PCC}=\frac{\sum_{i=1}^{n}(y_{i}-\overline{y})(\hat{y}_{i}-\overline{y}) }{\sqrt{\sum_{i=1}^{n}(y_{i}-\overline{y})^{2}}\sqrt{\sum_{i=1}^{n}(\hat{y}_ {i}-\overline{y})^{2}}} \tag{21}\]  
where \(y_{i}\) and \(\hat{y}_{i}\) are the \(i\)th measured and predicted response of VIV events, respectively; \(N\) is the number of samples; and \(\overline{y}\) and \(\widetilde{y}\) are the mean of the measured and predicted response of VIV events, respectively. MAE, RMSE and WMAPE can all be used to evaluate the amplitude difference between the measured response and predicted response. MAE values differences equally, whereas RMSE puts more emphasis on large differences. WMAPE provides a normalized evaluation, and the weight employed in the WMAPE is used to increase the significance of the error of large amplitude. With MAE, RMSE and WMAPE being closer to 0 and PCC being closer to 1, the prediction accuracy is higher.