esa-sceva/satcom-chunk-collection huggingface dataset:
# Publish/Subscribe-Middleware-Based Intelligent Transportation Systems: Applications and Challenges
## 4 Application of Publish/Subscribe Middleware in ITS
### Communication Technologies
#### 4.3.3 Information-Centric Network (ICN)  
Information-Centric Networking (ICN) is an innovative technology that facilitates content retrieval through a name-based communications protocol, detaching content from original servers and creating new access points using in-network caches on intermediate routers or network nodes [96]. This method reduces congestion and enhances delivery performance by embedding multicasting, multipath communications, and security at the protocol level. Researchers have shown that ICN improves ITS through [97]: content-based routing, which addresses mobility challenges, a Dynamic Naming approach for efficient network sensing, and hierarchical naming for flexible and precise object tracking. Furthermore, ICN optimizes routing space utilization with a block centralized storage structure, improving overall sensing efficiency within the ITS network. However, the challenges of scaling the architecture, deploying it on today's Internet, and securing it against new threats are non-trivial.  
[PERSON] et al. [98] developed the BONVOYAGE Communication System, an innovative communication system developed within the European H2020 BONVOYAGE project, focusing on optimizing multimodal and door-to-door transport for passengers and goods. The system uses the Internames principle of the ICN communication paradigm, DATEX II standard [99, 100], request-response, and publish/subscribe schema to disseminate static, real-time, and travel-centric content. The network consists of data producers (public or private transport operators publishing travel-centric information) and data consumers (end users who collect it). Experimental tests have shown that BONVOYAGE significantly reduces data processing for Data Consumers, resulting in a substantial decrease in bandwidth consumption and achieving an 80% reduction in latency and a 60% reduction in bandwidth consumption compared to baseline mechanisms. Although the proposed system is robust and comprehensive, its architectural complexity may pose configuration, integration, and long-term maintenance challenges. Moreover, as acknowledged by the authors, the system's routing, security, and QoS mechanisms require further enhancement to ensure reliability and scalability in real-world deployments.
# Abnormal Behavior Detection in Uncrowded Videos with Two-Stream 3D Convolutional Neural Networks
## 5 Experimental Works
### Analysis of Detection Errors
#### 5.3.4 Discussion of Detection Errors  
A thorough insight into different patterns found in each of the abnormal acts led to accurately addressing the training needs of the system with regards to each pattern. This has resulted in a continuous improvement in performance during the development of the recognition model. By providing appropriate data related to each pattern, the system has been able to mitigate the problems related to variance pertaining to a single behavior. However, considering the overlapping between acts of three classes, most of the cases of misclassification are found to be intrinsic rather than accidental. So far as the other sources of errors described above are concerned, they could be classified into two main groups after a detailed analysis.  
_Rare appearance of certain patterns in the datasets:_ the network essentially finds it difficult to learn the patterns belonging to each class that do not appear in many examples. Some examples of such rare occurrences include falling after swinging for some time, falling while grabbing an object, and detecting a behavior in a distant scene. Insufficient examplesof specific patterns can be observed as the main cause of a vast majority of the existing false negatives, as in some cases no other explanation could be found except that the system is unable to learn the features required to detect that pattern.  
_Limitations related to quality of videos:_ the quality of video and resultant frames is an inherent feature of the underlying datasets. It was observed that in some cases (e.g., hockey fights shot from a long distance), the optical flow algorithm was unable to capture the movement of players correctly.
# On the Rate-Distortion Theory for Task-Specific Semantic Communication
## 6 Conclusions  
In this paper, we have investigated the fundamental limits of task-specific semantic communication by extending the traditional rate-distortion theory to incorporate semantic distance constraints, as measured by a general divergence metric. We have considered a semantic communication model where the encoder accesses the semantic information source only through indirect observations, while both the encoder and the decoder utilize available side information. Our framework specifically addresses classification and generation tasks in task-specific semantic communication systems. We have derived the  
Figure 8: Experimental semantic rate-distortion functions for classification tasks on MNIST dataset. (a) Rate-distortion curves; (b) Three-dimensional surface plot.  
closed-form expressions for Gaussian semantic rate-distortion functions under various divergence metrics, revealing a fundamental three-way tradeoff involving transmission rate, bit-wise distortion, and distribution-wise semantic distance. Experimental validation using image-based semantic sources confirms these theoretical findings.  
Conceptualization, J.C., H.Z., Y.X., G.S., and P.Z.; methodology, Y.X.; software, J.C.; supervision, H.Z., Y.X., G.S., and P.Z.; validation, J.C.; writing--original draft, J.C.; writing--review & editing, Y.X. and J.C. All authors have read and agreed to the published version of the manuscript.  
The work of [PERSON], [PERSON], and [PERSON] was supported in part by the Mobile Information Network National Science and Technology Key Project under grant 2024 ZD1300700. The work of [PERSON] was supported in part by the National Natural Science Foundation of China (NSFC) under grant 62293483. The work of [PERSON] was supported in part by the NSFC under grants 62293480 and 62293481.  
Not applicable.  
The MNIST and CIFAR10 datasets used in this study are publicly available through the PyTorch library. The datasets can be accessed via the PyTorch official documentation: [[https://pytorch.org/vision/stable/datasets.html](https://pytorch.org/vision/stable/datasets.html)]([https://pytorch.org/vision/stable/datasets.html](https://pytorch.org/vision/stable/datasets.html)).  
The authors declare no conflicts of interest.
## 5 Conclusions  
This paper deals with the acid activation of clay minerals. It is concluded that a deep interlayer modification occurs even at low percentage of H\({}_{2}\)SO\({}_{4}\). Spectroscopy analysis by infra-red (IR),  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline  & \multicolumn{4}{c}{**Langmuir Constants**} \\ \cline{2-5}
**Samples** & \(q_{\text{max}}\) & \(K_{L}\) & \(\delta q\) & \(\Delta q\) **(\%)** \\  & (mg/g) & (L/mg) & \(\delta q\) & \(\Delta q\) **(\%)** \\ \hline
**S\({}_{0}\)** & 241.91 & 11.019 & \(9.77\times 10^{-3}\) & 2.63 \\
**S\({}_{1}\)** & 198.36 & 2.096 & \(2.29\times 10^{-3}\) & 1.82 \\
**S\({}_{2}\)** & 168.59 & 5.115 & \(0.19\times 10^{-3}\) & 0.13 \\
**S\({}_{3}\)** & 93.42 & 0.577 & \(5.00\times 10^{-3}\) & 2.42 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Estimated parameter values of the Langmuir model for the raw material (S\({}_{0}\)) and acid-activated clay samples (S\({}_{1}\), S\({}_{2}\), S\({}_{3}\)).  
X-ray diffraction (XRD) and chemical analysis have shown that chemical structure of clay changes from a partially crystallized state to amorphous silica state at a great extent of acid-activation. This result was confirmed by the disappearance of the interstratified phase illite/smectite, which leads almost to a total destruction of natural clay structure. During acid-activation, a part of octahedral cations (magnesium, iron, aluminum) was dissolved, creating new acid sites and the interlayer exchangeable cations are replaced by protons. The content of three oxides Al\({}_{2}\)O\({}_{3}\), Fe\({}_{2}\)O\({}_{3}\) and MgO of the raw material decreased from 14.27; 6.07; 1.46 to 9.4%; 1.89%; 0.43% for severe acid-treatment (S\({}_{3}\)), respectively. Acid attack process mainly affected the smectite phase of clay and caused a broad pore distribution of material, created mesopores and increased external surface area of clay. Acid-activation of clay minerals reduces the adsorption capacity of methylene blue used as cationic dye; this is attributed to the competition between the H\({}^{+}\) ions and dye cations to reach the surface. Also, this clear decrease could be a consequence of the partial collapse of the clay lattice to some extent of acid activation.  
[PERSON] conceived, designed and performed the experiments and wrote the first draft of the manuscript; [PERSON] and [PERSON] analysed the data and revised the manuscript; [PERSON] and [PERSON] revised the interpretation of the data and contributed to the revision of the manuscript; [PERSON] supervised the research work.  
This research was funded by King Khalid University, Abha-KSA.  
The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University, Abha-KSA, for funding this work through General Research Project under grant number (G.R.P-358-38).  
The authors declare no conflict of interest.
# A Novel Method of Adaptive Kalman Filter for Heading Estimation Based on an Autoregressive Model
## 6 Position Estimation of PDR Algorithm  
### The Principle of PDR Algorithm  
The PDR algorithm includes four procedures: Step detection, step-length estimation, heading estimation, and position calculation. In this paper, peak detection is used for step detection, and the time interval of two peaks is considered as the time of a step. A linear model is used to estimate the step-length [39].  
\[SL=a*F+b+v \tag{49}\]  
where \(SL\) is the step-length, \(F\) is the step frequency, \(v\) is noise, and \(a\) and \(b\) are coefficients dependent on human motion.  
After step detection, step-length estimation, and heading estimation are obtained, the position can be calculated as follows.  
\[\left\{\begin{array}{l}N_{k}=N_{k-1}+SL_{k-1}\cos\varphi_{k-1}\\ E_{k}=E_{k-1}+SL_{k-1}\sin\varphi_{k-1}\end{array}\right. \tag{50}\]  
where \(N\) and \(E\) are the north and east direction positions, respectively.
## 1 Introduction
The seismic response prediction ML model developed in this study aims to evaluate the seismic fragility of MCC (Motor Control Center) cabinets installed in the OPR1000 auxiliary building. To achieve this objective, a database was established by calculating the floor response spectrum around the resonance frequency of the MCC cabinet on the floors where these cabinets are installed. Figure 1 illustrates the percentage breakdown of various ML methods applied in structural engineering [12]. The analysis categorizes these methods into seven groups: neural networks (NN), Support Vector Machines (SVM), Boosting Algorithms (BA), Regression Analysis (RA), Random Forest (RF), Decision Trees (DT), and a miscellaneous group (including k-Nearest Neighbors (kNN)). Except for RA, six machine learning algorithms were utilized to develop ML models for seismic response prediction. The accuracy of these ML models was quantitatively assessed using metrics such as root mean square error (RMSE), mean square error (MSE), mean absolute error (MAE), and the coefficient of determination (R\({}^{2}\)). Given the extensive range of input variables representing seismic load characteristics and changes in material properties in this study, the minimum database size required to develop the ML models is considerably large. This requirement imposes greater demands on time and effort for database construction. To address this challenge, the study explored strategies to reduce the necessary database size and evaluated the prediction performance of the ML models through the reduction in input dimensionality [27]. Feature selection and extraction were employed as methods to reduce input dimensionality. Existing studies have primarily focused on relatively simple building structures or have overlooked the influence of material aging, making it difficult to apply their findings directly to complex infrastructure such as nuclear power plants. Addressing these gaps, the present study is expected to contribute to the advancement of ML-based seismic response prediction by (1) applying machine learning techniques to a structurally complex NPP structure, (2) incorporating aging-related material degradation through probabilistic variation of RC properties, and (3) demonstrating the potential to achieve accurate predictions with a reduced training dataset by optimizing input dimensionality, thereby improving the efficiency of data generation, which is typically time-consuming and resource-intensive in simulation-based studies. These efforts are anticipated to enhance the applicability and scalability of machine learning approaches in the seismic assessment of aging nuclear structures.
## 5 Conclusions  
In this research, we have proposed secured data aggregation and authentication-optimized methods in a cluster-based based network design (CBND) deployed in an underwater vehicular wireless sensor network termed UVWSN. UVWSN utilizes application monitoring and the inspecting of a vehicle or ship structures for the navigational system in the ocean. Performance metrics of the network depend on reliability, energy efficiency, and the average packet drop of the USNs (underwater sensor nodes) based on multiple mobile sinks (MMSs) deployed in the CBND UVWSN. Performance metrics assessment depends on malicious node attacker (MNA) detection in the network. Performance metric measurements, including energy efficiency and average packet drop evaluation in our proposed SDAA protocols, were determined and compared with the other contending standard secure MAC protocols, such as SEFSC, SDA, and EEST, deployed in similar simulation conditions in the network.  
Models of the proposed SDAA protocols were developed. They were deployed in the UVWSN scenario, which evaluates the network in the same condition as the other contending secure MAC protocol for measuring reliability, packet drop, delay, and energy efficiency in the network. In analyzing the simulation results based on the model developed for the proposed SDAA protocol, the proposed SDAA protocol guarantees higher data security in terms of reliability, packet drop, end-to-end delay, and average energy consumption. Therefore, the proposed SDAA protocol achieved higher energy efficiency of 30 J, less packet drop ratio, and less delay, as compared to the other contending standard secure MAC approaches, including EEST, which had 44 J, SEFSC had 37 J, and SDA had 42 J.  
For future work, we plan to improve the proposed SDAA protocol by analyzing and detecting specific types of attacks, such as DDoS and collision attacks, at the MAC layer.  
Conceptualization, S.K.E.; methodology, S.K.E.; software, S.K.E.; validation, H.C., S.K.E. and A.E.; editing, S.K.E.; investigation, S.K.E.; resources. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding, only from University.  
The study was conducted in accordance with the Declaration of Helsinki, and approved by the Institutional Review Board of University of Bridgeport.  
Informed Consent Statement: Informed consent was obtained from all the subjects involved in the study.  
Availability Statement: Available upon request.  
The authors declare no conflict of interest.
# A Dynamic Edge Server Placement Scheme Using the Improved Snake Optimization Algorithm
## 5 Experiment Results and Analysis
### Parameterization  
The parameters used for the experiments are listed in Table 1. Among these parameters, \(\alpha\), \(h_{1}\), \(h_{2}\), and \(N_{0}\) generally take fixed values. When \(P\) is larger, the transmission rate is faster and the transmission delay is shorter. When \(l\) is larger, the transmission rate is slower and the transmission delay is shorter.
## 5 Conclusions and Future Outline  
In recent years, there has been a boom in techniques for understanding spatial targets from the image domain; however, little research has been done on the electromagnetic and polarization properties of their components. In this paper, the polarization characteristics of three components are analyzed based on the electromagnetic data of a relatively simple structural space target. A novel method for interpreting the structure of space targets using polarization information obtained from PollSAR data is proposed. This algorithm presents a potential solution for analyzing the structures of space targets by utilizing echo data rather than relying on imaging and computer vision techniques. Compared to methods that use canonical scatterers as labels, the interpretation results of our method exhibit a superior mapping relationship with the actual structure of the target.  
In future studies, we will utilize additional kinds of complex space target structures, such as satellites, to further prove the effectiveness of our proposed methodology. In addition, modeling simulations for rough surfaces will be enhanced during the target modeling process. Optimization strategies such as parallel processing will be applied to decrease the electromagnetic calculation costs. A comprehensive dataset of component types suitable for deep learning will be established. To address the inefficiency of polarimetric data acquisition, solutions such as Generative Adversarial Networks (GANs) will be explored to achieve data augmentation, interpolation, and advanced data generation. Additionally, alternative classification methods such as transformer-based architectures or Graph Neural Networks (GNNs) will be investigated, as GNNs may offer better performance in handling complex spatial relationships and high-dimensional polarization data.  
**Author Contributions:** Conceptualization, J.C.; methodology, Z.C. and Z.X.; software, Z.C. and Q.W.; validation, Z.C. and X.A.; formal analysis, Z.C. and X.L.; writing--original draft preparation, Z.C.; writing--review and editing, Z.X., Z.C. and J.C. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by the National Natural Science Foundation of China under Grant 62401580 and by the Scientific Research Program of National University of Defense Technology under Grant ZK24-13.  
**Data Availability Statement:** No new data were created or analyzed in this study. Data sharing is not applicable to this article.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## 4 Sub-Optimal STMD with Clipped Viscous Damping-Stiffness Control Force
### Energy Dissipation Resulting from Clipping
This feature holds true only for harmonic motion. The equivalent model, represented by equations (44) and (45), will be used later to analyze the frequency responses of the STMD. In Figure 6, sample calculations of the equivalent viscous damping and the equivalent stiffness are presented for the sub-optimal STMD with \(\mu=1\%\) and \(K=3\). The calculations were conducted using the sample parameters listed in Section 3.5. The greater the difference between \(\omega\) and \(\omega_{1}\), the more the degree of clipping increases. Consequently, both the amount of additional energy dissipated and the resulting equivalent viscous damping coefficient \(c_{eq}\) rise. As an effect, \(c_{eq}\) significantly exceeds \(c_{a}\) (Figure 6a). In the case of stiffness (Figure 6b), the resulting equivalent stiffness, \(k_{eq}\), is noticeably smaller in magnitude than \(k_{a}\). Furthermore, the difference between \(k_{a}\) and \(k_{eq}\) becomes larger as the difference between \(\omega\) and \(\omega_{1}\) increases. The cumulative impact of clipping on both damping and stiffness adversely affects the performance of the sub-optimal STMD, as will be demonstrated in the following.
# Analysing Land Cover Change in the Valencian Community through Landsat Imagery: From 1984 to 2022
## 2 Materials and Methods
### Validation
#### 2.5.4 Comparison with Copernicus Global Land Cover  
The Copernicus Global Land Service (CGLS) annually delivers a dynamic global land cover product, CGLSLC100, with a spatial resolution of 100 m. The third collection of this product, spanning from 2015 to 2019, is derived from PROBA-V satellite observations and ancillary datasets ([PERSON] et al., 2019; [PERSON] et al., 2019; [PERSON] et al., 2019) and has an estimated accuracy of approximately 80% ([PERSON] et al., 2019; [PERSON] et al., 2019). It comprises a total of 23 classes that we reorganised to facilitate comparison with the 2015 product obtained through our methodology. We grouped closed forest classes, open forest classes, scrubland, herbaceous wetland, moss, and lichen classes into Dense Vegetation. Herbaceous Vegetation and Cropland classes were included in Sparse Vegetation, while Built-up Areas represented the Built-up class. Water bodies encompass the Snow and Ice and Permanent Water Bodies classes, and the Bare/sparse Vegetation class falls under thecategory of Other. Additional information on the Copernicus Global Land Cover can be found in the User Manual [61].
# Experimental and Numerical Evaluation of Toxic Pool Evaporation
## 5 Proposition of an Evaporation Model
### Development of a Predictive Tool: EVAP-Tox  
To take this phenomenon into account, we require a specific numerical tool. This tool should allow us to solve the evaporation rate coupled with the pool behavior in terms of concentration and temperature. The skeleton of this model (EVAP-Tox) is represented in Figure 15.  
Figure 15: Skeleton of evaporation model.  
Input data for the model are the pool dimensions, its depth, the ammonia pool concentration, the pool, and ambient temperature, assuming both the pool temperature and ambient temperature are identical, the air velocity, and the required simulated time. The four computation steps are detailed below.  
First, the vapor pressure is estimated considering both pool surface temperature and concentration following the curve proposed by [23].  
Then, the evaporation rate, \(\omega_{k}\) in the equation, is estimated using the Mackay and Matsugu correlation. In this relation, the vapor pressure is the one computed with the pool surface characteristics. This also enables the estimation of the sink term of ammonia and energy for the pool surface.  
The concentration and temperature gradients inside the pool are then estimated, following a 1D finite difference discretization. The equation is solved using a first-order scheme in time with a full explicit approach, and a second-order finite difference scheme for the diffusion term in space. The species concentration equation (Equation (3)) is simplified to a diffusion relation, assuming that convection is negligible with the assumption of constant density:  
\[\frac{\partial Y_{k}}{\partial t}=D_{k}\frac{\partial^{2}Y_{k}}{\partial x_{i }^{2}}+\omega_{k} \tag{4}\]  
It must be added that the liquid density rises when the ammonia concentration diminishes with a convective movement in the pool. A constant was then added to increase the diffusion velocity while keeping a 1D approach. The constant was found to be 50 using the small-scale reference test; its value was then kept constant for comparison with other cases.  
The temperature, \(T\), is also solved using a diffusion equation:  
\[\frac{\partial T}{\partial t}=\frac{\lambda}{\rho\cdot C_{p}}\frac{\partial^ {2}T}{\partial x_{i}^{2}}+\omega_{tk} \tag{5}\]  
In this equation, \(\lambda\) is the thermal conductivity, \(C_{p}\) is the specific heat of ammonia, and \(\omega_{tk}\) is the sink term of energy, since for this equation the cooling is due to the phase change process. As for species, this equation is solved using an explicit approach--first order in time and second order in space using a finite difference scheme.