esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Blockchain-Based Supply Chain Security Solutions and Integration Models
### Data Privacy with Zero-Knowledge Proof (ZKP) and Confidential Transactions
Recent high-profile blockchain bridge attacks have underscored the severity of these risks. As demonstrated in Figure 7, security risks associated with cross-chain transactions can be summarized as follows: Centralized bridge structures rely on single verification mechanisms, making them attractive targets for attackers and introducing risks to cross-chain asset transfers. Attackers exploiting smart contract vulnerabilities may manipulate asset transfers, steal funds, or execute fraudulent transactions. Oracle manipulation poses another significant risk, enabling the introduction of false or fraudulent data into supply chain validation processes, potentially allowing counterfeit products to enter the system and reducing the overall reliability. If data security from oracles cannot be ensured, the integrity of the entire chain is compromised, creating opportunities for fraud. Additionally, incompatibility between consensus algorithms used by different blockchain networks may introduce vulnerabilities during validation processes, complicating inter-chain interactions and negatively affecting system reliability [99]. To mitigate these risks, cross-chain bridges should adopt decentralized verification mechanisms. Techniques such as multi-signature and threshold encryption can enhance security by preventing a single authority from gaining control over cross-chain transaction validations. Additionally, secure software development processes such as formal verification can be employed to identify vulnerabilities in cross-chain smart contracts. Auditing smart contract codes helps detect potential security risks, such as re-entrancy attacks and integer overflow/underflow, in advance. Regarding oracle security, it is essential to adopt distributed oracle architectures. Instead of relying on a single centralized oracle provider, utilizing multiple independent oracle sources can increase the accuracy and reliability of data. Moreover, integrating cryptographic techniques, such as ZKP, can verify whether the oracle-provided data have been altered or tampered with. These methods reinforce data integrity within supply chains and help prevent fraud attempts. A parallel blockchain and smart contract-based cross-chain model for rice supply chain auditing has been proposed, employing a combination of hash lock + smart contract + relay-chain to secure data sharing. The K-means algorithm and SPBFT consensus mechanism improve the transaction speed and reliability, achieving lower costs, reduced latency, and effective auditing [100]. Similarly, an SCT-CC traceability system based on cross-chain technology utilizing Hyperledger Fabric with smart contracts and a multi-chain architecture has been proposed, significantly enhancing data integrity, transparency, and security. Testing results demonstrated query speeds of 110 TPS and write capabilities of 102 TPS, indicating the system's scalability and applicability [101].
## 3 Survey of Techniques for Updating
### The Execution Environment
#### 3.3.20 Dynamic Virtual Machine (DVM)  
The DVM [74] architecture provides a dynamically modifiable virtual machine on top of SOS. Event handlers are written in a portable bytecode scripting language, and Operation Libraries implement its instruction set. A Scheduler is used to control script execution. An event manager mapsevents to handlers, allowing new events to be defined dynamically. Both scripts and binary modules implementing an Extension Library can be updated (using for example Deluge to perform the dissemination) allowing new bytecodes to be introduced. A Resource Manager implements Admission Control is used to ensure that only compatible scripts that can fit in memory are admitted into the system. Scripts are installed (after a wait if required) only when there are no scripts executing.  
DVM provides no specific features related au autonomic updates, but as for Dynamic TOS, it provides additional granularity and performance improvements that may be useful features in an autonomic environment.
# Investigations on Interface Shear Fatigue of Semi-Precast Slabs with Lattice Girders
## 4 Fatigue Tests on Semi-Precast Slabs with Lattice Girders
### Test Results  
#### 4.5.1 General  
The fatigue loading caused a progressing crack formation with increasing deformation, crack widths, and steel strains in the reinforcement. As expected, the fatigue tests with several million load cycles showed a higher scatter compared to static tests. The scatter involves the failure mode, interface displacement, the stress of lattice girders, and the reached number of load cycle. The failure mode and the reached number of load cycles are listed in Table 5.  
Specimens with low interface reinforcement ratios realized by two rows of lattice girders (EG01-EG03, EG06, EG09, EG10, and EG12), except for Specimen EG09, resisted a minimum of \(N\) = 2.0 million load cycles. Independent of concrete strength and interface roughness, a combination of interface and shear failure occurred. Slabs with medium interface reinforcement ratios and three rows of lattice girders (EG04 and EG07) failed predominately through vertical shear failure with only small crack development along the interface. For tests with high interface reinforcement ratios (EG05, EG08, EG11, EG13, and EG14), only EG08 showed a clear shear crack after \(N\) > 2.0 million load cycles. For the other highly reinforced and highly loaded specimens, failure occurred generally due to anchorage failure of the lattice girder in the compression zone of the slab or failure of the anchorage of the longitudinal reinforcement at the support. In the following, the influence of interface roughness, interface reinforcement ratio, and steel strains are presented. An influence of the concrete strength could not be determined.
# Analysis of the Efficiency of Slope Evacuation for Dense Crowds in Urban Street-Type Public Spaces
###### Abstract  
Street-type spaces, characterized by their relative closedness and propensity for human congregation, inherently carry potential safety evacuation risks. In order to study the influence of slopes on the evacuation efficiency of pedestrians in street-type public spaces under the state of passenger flow surge during holidays, this study systematically analyzes the changing rules and behavioral characteristics of pedestrian evacuation in downhill movement through a three-phase analysis of the risk of crowd gathering in urban street-type spaces (before, during, and after) and evacuation simulation experiments combining variables such as slope, street width, obstacle layout, disability type, and group movement. The findings indicate that, in the structural design of street-type spaces, slopes of more than 4\({}^{\circ}\) should be minimized to maintain the smooth flow of pedestrians. Areas in streets with widths narrower than 2 m are high-risk zones for crowd gathering and should be better supervised. The number and location of obstacles should be reasonably arranged under the condition of satisfying the safety of pedestrians' passage. The differences in the ability of evacuees should be taken into account to improve evacuation system deficiencies and ensure that everyone can evacuate safely. Ultimately, we propose a preventive mechanism for the safe evacuation of urban street-type public spaces to reduce the risk of crowd gathering and safeguard pedestrians. This study provides a theoretical framework for understanding the dynamics of pedestrian evacuation in inclined street-type spaces, thereby guiding urban planners and public safety managers to enhance the design and management of such spaces.  
s +
Footnote †: journal: Applied sciences  
article
## 3 Results and Discussion
The dielectric loss did not increase at high temperature, unlike that of most ferroelectric ceramics, where the dielectric loss increases because of the increase in ionic mobility or a ferroelectric-paraelectric transition at high temperatures. The dielectric losses of the 0.9 BaTiO\({}_{3}\)-0.1(Bi\({}_{0.5}\)Na\({}_{0.5}\))TiO\({}_{3}\) ceramic were found to decrease significantly with the addition of Ta\({}_{2}\)O\({}_{5}\) and Nb\({}_{2}\)O\({}_{5}\). For instance, it decreased from 3.6% to 1.8% and from 3.9% to 1.4% at 25 \({}^{\circ}\)C when the Ta\({}_{2}\)O\({}_{5}\) and Nb\({}_{2}\)O\({}_{5}\) dopant content increased from 1.0 to 2.0 mol%, respectively, which is close to the values for common X7R and X8R ceramics with a similar thickness. The electrical resistivities of the 0.9 BaTiO\({}_{3}\)-0.1(Bi\({}_{0.5}\)Na\({}_{0.5}\))TiO\({}_{3}\) ceramics with added Ta\({}_{2}\)O\({}_{5}\) and Nb\({}_{2}\)O\({}_{5}\) obtained at 25 \({}^{\circ}\)C and 200 \({}^{\circ}\)C are also listed in Table 1. All samples appeared to be good insulators, with resistivities ranging from 2.6 \(\times\) 10\({}^{12}\) \(\Omega\cdot\)cm to 9.0 \(\times\) 10\({}^{12}\) \(\Omega\cdot\)cm at 25 \({}^{\circ}\)C and from 1.5 \(\times\) 10\({}^{10}\) \(\Omega\cdot\)cm to 1.3 \(\times\) 10\({}^{11}\) \(\Omega\cdot\)cm at 200 \({}^{\circ}\)C. There was a decrease of approximately two orders of magnitude in resistivity as the temperature rose from 25 \({}^{\circ}\)C to 200 \({}^{\circ}\)C.  
In this study, it was found that Ta\({}_{2}\)O\({}_{5}\) dopant was more efficient to increase the temperature stability of dielectric constant of the 0.9 BaTiO\({}_{3}\)-0.1(Bi\({}_{0.5}\)Na\({}_{0.5}\))TiO\({}_{3}\) ceramic, compared with Nb\({}_{2}\)O\({}_{5}\) dopant. Particularly, the host ceramic with 2 mol% Ta\({}_{2}\)O\({}_{5}\) dopant hadthe best composition in terms of dielectric properties. The X9R formulations and their dielectric properties were highly repeatable in this study, which was not the case with the lead-based X9R ceramics reported in the literature [10].
## 3 Results and Discussions
### Displacements
#### 3.3.2 GNSS Displacements  
Daily displacements orthogonal to the dam crest were averaged by applying a one-month-long moving window, thus showing its tendency in the long term (Figure 10). After applying a temporal low pass filter, the monitored temporal range is shortened. The zero of the displacements is assumed as the first day of the time series (DOY 120). Displacements increased in the springtime period to reach a platoon between DOYs 197 and 247 (about 2.6 mm). The maximum indicated by the sinusoidal fitting is on DOY 219, which is almost in phase with air and water surface temperatures. The peak is shifted compared to that obtained via PS-InSAR (22 days), although both GNSS and PS-InSAR displacements behaviours are characterised by two plateaus that partially overlap (\(\approx\) DOYs 220-230). The standard deviation of the differences between measured and interpolated values was \(\sigma_{\text{XY}}\approx\) 0.33 mm.  
Figure 9: The temporal behaviour (DOY in \(x\)-axis) of the horizontal total displacements orthogonal to the dam (\(D_{\text{TOT}}\), mm) is estimated via PS–InSAR (red dots). A sinusoidal interpolation curve (continuous dashed line) and a confidence band of \(\pm\)2 \(\times\)\(\sigma_{\text{XY}}\) between measured and interpolated values (dark grey lines) are reported to facilitate the interpretation of the phenomenon.
## 3 Construction of the WOA-Attention-BILSTM  
Parameters of the Attention-BILSTM model, such as the number of neurons, time step, number of epochs, and learning rate, have a strong influence on the experimental results. For example, the time step is reflected in the model as the number of recurrent units of Attention-BILSTM. If the time step is set too small, it will lead to an insufficient representation of the information correlation. Setting the time step too large will result in fewer data sets and insufficient learning of the neural network. These parameters are often set based on manual experience and optimized in experiments, and if these parameters are set inappropriately, it will lead to poor model performance. Therefore, in the model architecture design, this paper uses the WOA to optimize the Attention-BILSTM, and the specific steps are as follows:  
1. First, clean the AIS data, fill in the missing data using cubic spline interpolation, and then resample the data with a time interval of 60 s;
2. Normalize and divide data into training and test sets;
3. Determine the number of iterations, population size, and individual space dimension of the WOA; determine the learning rate, batch size, epoch, and number of hidden layer units as optimization objects; and determine the lower bound and upper bound for each object;
4. Randomly generate the population and obtain the parameters of the initial Attention-BILSTM;
5. The Mean Square Error between the target and predicted values of different populations is calculated and used as the fitness in training, and the parameter combination with the smallest fitness is selected as the optimal solution for the global optimal test. To make the parameters obtained by WOA optimization more suitable for Attention  
Figure 6: The structure of the Attention mechanism.  
BILSTM, the Mean Square Error in this paper is the weighted Mean Square Error of the training and test sets, as shown in Equation (21);  
\[Fitness=(1-k)\times\frac{1}{N}\sum_{i=1}^{N}\left(\hat{y}_{t}^{i}-y_{t}^{i} \right)^{2}+k\times\frac{1}{M}\sum_{j=1}^{M}\left(\hat{y}_{v}^{j}-y_{v}^{j} \right)^{2}. \tag{21}\]  
where \(k\) is the weight value and takes the value range [0, 1]. \(N\) is the total number of samples in the training set, \(M\) is the total number of samples in the test set, and \(\hat{y}_{t}^{i}\), \(\hat{y}_{v}^{j}\) are the predicted values of the training sets and test sets; \(y_{t}^{i}\), \(y_{v}^{j}\) are the target values of the training sets and test sets;  
* Start iterations to obtain the Attention-BILSTM network parameters. Update the Attention-BILSTM using the parameters;
* Repeat steps 5-6 until the maximum number of iterations is reached;
* Output the optimal results, use the obtained optimal network hyperparameters to build Attention-BILSTM for ship trajectory prediction.  
The architecture of the prediction model we built is shown in Figure 7.
# A Survey on Cybersecurity in IoT
## 6 Challenges and Future Directions
In conclusion, the challenges facing IoT cybersecurity are vast and varied, requiring a multidisciplinary approach that goes beyond technical solutions to encompass regulatory, economic, and operational considerations. Future research could explore cost-effective security solutions that do not compromise on protection, as well as innovative business models that incentivize investment in IoT security. Also, it should focus on developing adaptive, scalable, and cost-effective security solutions that can keep pace with the rapid evolution of IoT technologies. As the IoT continues to expand, the need for robust, integrated security measures will become increasingly critical, necessitating ongoing collaboration between industry, academia, and government to ensure the safety and resilience of the IoT ecosystem [282; 283; 284; 285; 286; 287]. The list of key topics related to challenges of cybersecurity in IoT and future directions is summarized in Table 6. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Topic** & **References** & **Description** \\ \hline Security Challenges in IoT & [251; 252; 253; 254; 255; 256; 257; 258; 259] & IoT cybersecurity faces diverse challenges driven by device heterogeneity, regulatory constraints, and application-specific requirements such as real-time protection in IIoT, privacy in wearables, and ultra-low-latency in autonomous vehicles. \\ \hline Legacy Devices and Security Risks & [260; 261; 262] & Focuses on the challenges posed by legacy IoT devices that were not designed with security in mind and potential solutions like retrofitting. \\ \hline Interoperability and Standardization & [263; 264; 265] & Highlights the challenges related to interoperability among IoT devices and the need for standardized communication protocols and security frameworks. \\ \hline Privacy and Data Protection & [266; 267; 268] & Addresses privacy concerns, emphasizing the need for advanced privacy-preserving techniques and secure data management in IoT. \\ \hline Decentralized Security Solutions & [269; 270; 271] & Discusses decentralized approaches like blockchain and DLT for enhancing security in distributed IoT environments. \\ \hline Emerging Technologies and IoT Security & [272; 273; 274; 275] & Explores the impact of emerging technologies like 5G and edge computing on IoT security and the associated new attack vectors. \\ \hline Regulatory and Economic Considerations & [276; 277; 278; 279; 280; 281] & Examines the regulatory landscape and economic factors influencing IoT security, emphasizing the need for international cooperation and cost-effective solutions. \\ \hline Future Research Directions & [282; 283; 284; 285; 286; 287] & Suggests future research directions focusing on scalable security solutions, adaptive strategies, and enhancing stakeholder collaboration for IoT security. \\ \hline \hline \end{tabular}
\end{table} 
Table 6: A summary of studies related to challenges and future directions of cybersecurity in the IoT.
# Towards a Maturity Model for IoT Adoption by B2C Companies
## 1 Introduction  
The Internet of Things (IoT) technologies have been present on the market for quite some time now and have already proven to bring massive benefits both for businesses and consumers. That is, IoT is used in smart manufacturing, logistics, energetics, healthcare, retail, home security and many more. Currently, the total number of interconnected objects amounts to 8.3 billion and is expected to increase to 21.5 billion by 2025 [1]. With the number of organisations adopting IoT products and services rapidly increasing, it is becoming easier to identify common difficulties they are facing in the adoption process. While information security and privacy are quite apparent points of concern, there are many more IoT-specific factors that need to be addressed for the implementation to be successful and actually generate value. For that matter, maturity models have been found to be effective for, firstly, the assessment and, secondly, the improvement in the process by breaking it down into highly detailed steps.  
Although the topic of IoT is highly popular in the academic research community, surprisingly few studies focused on exploring it in the Business to Consumer (B2C) field. Most of the existing maturity and assessment models are either developed for the business to business (B2B) field or specify the dimensions and characteristics in a very general way, without considering the details that might be crucial for a B2C context. That being said, the aim of this research is to cover the described gap in currently existing studies by developing a maturity model specifically for the B2C context. Apart from contributing to the scientific knowledge pool of IoT, this paper also brings added value from the practical point of view. Organisations wishing to adopt IoT technology or to increase the efficiency of its current use can directly apply the developments of this research.  
With this in mind, the research questions this thesis addresses are as follows:R1: How can B2C organisations assess their current position in the IoT adoption process?  
R2: How can B2C organisations achieve the next maturity level of IoT adoption?  
This paper begins with a synthesised literature review (Section 2), that firstly introduces the IoT concept and then covers the topics of maturity models. Section 3 provides a discussion on the methods employed in the research to develop the IoT adoption maturity model. Then, Sections 4-6 present the analysis of three main sources of model dimensions identification. The process of the maturity model development, as well as the final model itself, are presented in Section 7. Finally, Section 8 provides a discussion on this research, including limitations and future research possibilities.
# CycleGAN-Driven MR-Based Pseudo-CT Synthesis for Knee Imaging Studies
## 3 Experimental Results  
### Qualitative Results  
In this section, we present a qualitative evaluation of the synthesized pseudo-CT images of the knee compared to their corresponding ground-truth CT images. Visual inspection is crucial to evaluate the realism, anatomical accuracy, and overall quality of the synthesized pseudo-CTs. Figure 3 shows side-by-side comparisons for four representative subjects in the dataset to illustrate the outcomes of our knee pseudo-CT synthesis method.  
Anatomical inspection of the results shows the high quality of the synthesized pseudo-CTs and the robustness of the method when delineating the bone structures despite different anatomical distributions and body mass indexes, which is of utmost importance in our assessment. We inspected the representations of critical bone structures within the knee joint, encompassing the femur, tibia, fibula, and patella, to gauge the accuracy of the pseudo-CT images. Despite minor deviations in the HUs in the boundaries of the bone structures, as demonstrated in the error maps, the synthesized pseudo-CTs are able to capture the shape and details of the bone contours. As part of this evaluation, we can also see how the pseudo-CT method is able to capture and include the presence of implants with good accuracy.  
Realism and texture analysis are essential to this qualitative assessment. Visual comparisons between the pseudo-CT and CT images from these representative cases provide initial insights into the realism of the synthesized images. Particular attention is directed towards the preservation of tissue structures, such as bone surfaces, ligaments, and surrounding soft tissues, as well as the maintenance of textural details. Overall, the pseudo-CT method is able to capture and maintain the same distribution of the different tissues, despite presenting less detail for muscle and soft tissues, where there is a lack of detail in their shared boundaries and their intrinsic textures, compared to the ground-truth CT. These synthesis artifacts may be due to the limited number of MR-CT pairs in the dataset used for training our method.  
Additionally, we could also see how the CyclGAN is able to handle the generation of densities associated with implants, as could be seen in the synthesis of the screws in second patient in Figure 3.