esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on Blockchain Data Availability and Storage Scalability
## 2 Related Work
### Scalable Optimization of Blockchain Storage  
The solutions for optimizing the scalability of blockchain storage are mainly divided into three categories: data sharding, data reduction, and multi-node-type collaborative storage.  
1. Data sharding: The data is divided so that each node stores partial data. The ZILLIQA model [3] implements network sharding, transaction sharding, and smart contract sharding, with different shards stored on different nodes. The ElasticChain [4] model divides the blockchain into multiple segments and backs up data on different nodes on a segment-by-segment basis.
2. Data reduction: Based on the consideration of the importance of block data, by deleting some block data, the storage pressures of full nodes are alleviated. The Block File Pruning [5;6] allows all nodes to discard block files while preserving the UTXO index. [PERSON] [7] proposed the rolling block method--requiring miners to add checkpoints in the block--in which all blocks older than 30 days are deleted.
3. Multi-node-type collaborative storage: Multiple types of nodes work together to maintain blockchain operations. Examples of such storage include Storage Scheme with Full Nodes and Lightweight Nodes (SSFL) [8;9], Storage Scheme with Full Nodes and Enhanced Lightweight Nodes (SSFE) [10; 11; 12], etc.  
The blockchain scalable storage model is a summary of and improvement on blockchain scalable storage schemes. On the basis of ensuring that nodes have verification functions, it uses data sharding to store block data, reducing data redundancy while giving nodes complete functionality. For example, it uses the Jigsaw-like Data Reduction [13]. Adopting the Efficient Block Validation method, which is based on state data reduction, it effectively reduces the amount of data that nodes need to store while ensuring node verification capability, significantly reducing the amount of saved data, and improving the scalability of blockchain storage.  
Although the above studies can effectively improve the storage scalability of blockchain, they do not explicitly guarantee the availability of data.
## 4 Compatible Structures in the Point Cloud Registration Problem
### COS in Rotation Search
**Proposition 2** (3-COS, invariants and constraints). : _Assume that now we obtain a new correspondence, say the \(3_{rd}\) correspondence, in addition to the 2-COS we have, as illustrated in Figure 2 we are able to construct a 3-COS with two more invariants such that:_  
\[l_{i3}^{l}=|\,||\mathbf{u}_{i}^{*}-\mathbf{u}_{3}^{*}||-||\mathbf{v}_{i}^{*}- \mathbf{v}_{3}^{*}||\,|\,|=e_{i3}^{l}, \tag{16}\]  
_and two additional length-based constraints are satisfied:_  
\[l_{i3}^{l}\leq L, \tag{17}\]  
_where \(i\in\{1,2\}\). Apart from simply constraining the length-based rigidity, we also supplement a residual-based constraint on \((\mathbf{u}_{3},\mathbf{v}_{3})\) such that:_  
\[\|\mathbf{R}_{12}\mathbf{u}_{3}-\mathbf{v}_{3}\|\leq G, \tag{18}\]  
_where \(G\) is the bound for the residual error w.r.t. the \(3_{rd}\) correspondence. Besides, for every single 2-COS in this 3-COS, we can compute a raw rotation, represented as \(\mathbf{R}_{ij}\) with the correspondence tuple \((i,j)\in\{(1,2),(2,3),(1,3)\}\). In this fashion, we can impose new constraints on the mutual compatibility between the raw rotations w.r.t. different correspondence tuples selected from this 3-COS such that:_  
\[\angle\big{(}\mathbf{R}_{ij},\mathbf{R}_{ab}\big{)}\leq F, \tag{19}\]_where \"\(\angle\)\" is the geodesic distance (error) [51] between two rotations, written as:_  
\[\angle\big{(}\mathbf{R}_{ij},\mathbf{R}_{ab}\big{)}=\left|arccos\left(\frac{tr\big{(}\mathbf{ R}_{ij}^{\top}\mathbf{R}_{ab}\big{)}-1}{2}\right)\right|, \tag{20}\]  
_where \(F\) is the bound of noise for the geodesic error satisfying that \(2\angle(Exp(\mathbf{\rho}),\mathbf{\mathrm{I}}_{3})\leq F\) and \(\mathbf{\mathrm{I}}_{3}\) denotes the \(3\times 3\) identity matrix._  
Proof. : The proof of Constraints (17) and (18) is similar to that in Proposition 1, thus omitted here. For the constraints (19), on the basis of the triangular inequality of the geodesic distance in [51] and similar to [27], we can derive:  
\[\angle(\mathbf{R}_{ij},\mathbf{R}_{ab})=\angle\Big{(}\mathbf{R}Exp(\mathbf{\rho} _{ij}),\mathbf{R}Exp(\mathbf{\rho}_{ab})\Big{)} \tag{21}\] \[=\left|arccos\left(\frac{tr\big{(}Exp(\mathbf{\rho}_{ij})^{\top}\mathbf{R }^{\top}\mathbf{R}Exp(\mathbf{\rho}_{ab})\big{)}-1}{2}\right)\right|\] \[=\angle\Big{(}Exp(\mathbf{\rho}_{ij}),Exp(\mathbf{\rho}_{ab})\Big{)}\] \[\leq\angle\Big{(}Exp(\mathbf{\rho}_{ij}),\mathbf{\mathrm{I}}_{3}\Big{)} +\angle(Exp(\mathbf{\rho}_{ab}),\mathbf{\mathrm{I}}_{3})\leq F.\]  
Up to now, in rotation search, the constraints over the 2-COS and 3-COS have been established, which will be later converted as a series of Boolean conditions for the sampling and filtering frameworks of ICOS in Section 5.
# Patient Safety Culture in Hospital Settings Across Continents: A Systematic Review
## 5 Limitations and Future Research Directions  
This study has several limitations. Only the articles that utilized the HSOPSC as the main instrument were included, leading to the exclusion of other tools used to assess patient safety culture, as well as studies conducted in non-hospital settings. This may limit the scope of the findings across other healthcare environments and measurement approaches. The differences in cultural background, or how studies are conducted, as well as the weight of each study, were not considered. The analysis was applied to the studies that had PRR% as the main measure. The scanning process was influenced by the English language inclusion criterion, which excluded papers from East Asian countries like South Korea and China, and Spanish-language papers from South America. Even though this analysis provides an overview of the state of PSC across different countries, the analysis relied on 70 surveys, which might not represent all countries globally.  
In terms of future work, this study offers several recommendations. To advance research on PSC fields, studies should be detailed and adhere to AHRQ guidelines [102].  
Establishing a standardized protocol for conducting PSC research is recommended to facilitate comparisons. The differences found in PSC scores across continents imply that customized improvement strategies may be needed for each region. To gain a deeper understanding of the evolution of patent safety culture, a comprehensive analysis should be conducted over time in order to improve the quality of healthcare outcomes, follow trends, identify the factors that influence patient safety, and apply interventions based on evidence. Although the HSOPSC instrument is widely utilized on a global scale, there is a notable lack of studies employing this instrument within the Australian healthcare context, suggesting the need for future research to address this gap.
# Design and Analysis of a Novel Variable Stiffness Joint Based on Leaf Springs
The reconfigurable variable stiffness brake RVSA [31] can successfully execute stiffness reconstructions and online adjustments; however, each reconstruction requires the disassembly of the entire joint structure, which can have an adverse effect on its accuracy. In addition to traditional driving methods, new approaches based on the characteristics of the applied materials are receiving more and more attention, such as shape memory alloys (SMA) [32], dielectric elastomers [33], magnetorheological fluids [34], etc. Based on this principle, different drivers have been developed, such as linear digital variable stiffness actuator (LDVSA) [35], bionic variable stiffness dielectric elastomer actuator (VSDEA) [36], and magnetorheological fluid variable stiffness driver (VSAMF) [37]. In this paper, a novel reconfigurable VSJ is designed. The joint uses an arc-shaped groove to continuously adjust the stiffness, while a reconfiguration mechanism is designed for use within the joint in order to calculate the number of leaf springs. Discrete stiffness changes can be achieved by simply adjusting the number of leaf springs, thereby providing a wider range of stiffness and load capacities, enabling various working conditions to be met. The remaining chapters of this paper are organized as follows: Section 2 introduces the principle of stiffness adjustment for VSJs, the stiffness model, and joint characteristics. In Section 3, the structure of the VSJ is introduced. In Section 4, simulation analysis and performance evaluation of the joint are conducted. Finally, the conclusions of the paper and potential future works are presented in Section 5.
# Removal of Antibiotics by Biochars: A Critical Review
## 5 Knowledge Gaps, Critical Evaluation, and Future Directions
As mentioned earlier, the adsorption of antibiotics using biochars leads to the production of secondary waste, either due to the concentration of antibiotics in the biochar or through metal-doping-related secondary pollution. It is highly unlikely to reuse the antibiotic-loaded biochars, except for molecularly imprinted biochars. Therefore, they should not be used for soil amendment, as antibiotics may leach from the biochars and reach surface waters. However, it should be noted that antibiotic leaching depends on the biochar type, biochar-antibiotic interaction, and environmental conditions. Therefore, high antibiotic retention may be observed in different biochars, as in the case of activated carbons [183]. In fact, this problem is not exclusive to biochars. All adsorbents, membranes, and activated sludge also confront this post-antibiotic-removal problem. The most frequently used adsorbent, activated sludge, is incinated to avoid secondary pollution. In recent years, it has not been allowed for fertilizer use in some countries [60]. A thermochemical conversion strategy can be applied to antibiotic-loaded biochars by using high temperatures to produce activated carbons and to remove the antibiotics. The activated biochars can be used again for the adsorption of antibiotics. The activated carbons can be incinated similarly to activated sludge once they have been used for antibiotic removal. The main problems of biochars are their small particle size and low density, which make it very difficult to remove them from the water after the adsorption [184]. The small particle size and low density of biochars lead them to be suspended in water, preventingsolid-liquid separation or settling. Magnetic modification seems to be the unique solution to this problem.
## 5 Discussion and Future Work
### Limitations and Future Work  
This study also encountered some limitations. First, the dataset categories are not balanced. The presence of minority class labels (Class V) in the stadium fire risk dataset has not been practically addressed. To handle training data with minority class labels, it is common to resort to the widely adopted solution of resampling minority class instances. However, no matter how effective the resampling process is, it can seriously alter the original distribution of the data (if our goal is to balance the training set). The main reason is that during the sampling process, a large number of potentially very useful majority class label (Class II, Class III) instances may be inevitably removed from the training set, which reduces the generalization of the model. In this work, we attempt to partially ameliorate the above-mentioned data class imbalance importance problem (stratified sampling cross-validation scheme in Section 3.4.2) by evaluating the trained model on a validation set that is closer (in terms of size) to the initial data size, aiming to preserve as much of the initial data distribution as possible in this way. While some positive signs regarding the value of such a process have been identified (see Section 4.4), overall it did not significantly improve the model's effectiveness, and potential improvements need to be further investigated and planned in our future work.  
Second, the dataset is relatively single. The fusion of large amounts of other types of data (e.g., fire images, time series observations) may improve model performance. Third, due to the scarcity of data, the stadium assessment only refers to the fire risk, which obviously affects the usefulness of the model. If more accurate property loss, casuality, social impact and other indicator data can be incorporated into the prediction model, the application value of this method will be greater. Furthermore, in future work, as experts acquire data, methods are needed to reduce the subjectivity of expert opinions. Moreover, deep neural networks and deep learning methods can be used and compared with machine learning methods. For instance, [PERSON] et al. (2019) proposed a Deep Belief Network (DBN) with Recurrent LSTM Neural Network (R-LSTM-NN) for predicting fire hazard values in smart cities, which has an accuracy of 98.4% that is higher than our optimal model of 93.2%. With more data, the performance of deep learning methods may improve significantly.
# V2X Communication over Cellular Networks: Capabilities and Challenges
## 6 Open Issues and Future Trends
Considering UCs such as \"Remote Driving\" and \"Cooperative Driving\", multiple networks may need to communicate with the application servers located in the core network and in these cases, PC5 will not really be an option at all. Another critical factor that needs to be addressed is the significant improvement of the spectral efficiency for the uplink and sidelink. This means that the vehicles should be equipped with more advanced modems capitalizing the benefits of Multiple Input-Multiple Output (MIMO) systems. Ultra-densification of the network is the obvious solution for supporting the advanced V2X UCs. However, to support such a number of vehicles in an urban environment would require a significant number of BSs, the Capital Expenditure (CAPEX) of which will be significant. Moreover, this would result in a significant number of HOs and a dramatic increase of the respected signaling. This suggests that new HO schemes should be considered, together with the solutions, in order to increase the computing performance at the edge of the network [53] and centralized RAN solutions. A possible solution for handling HOs would be to use contextual information about the movement of the vehicles, as mentioned before. In this way, the access network can act proactively and minimize any disruption time, without requiring dual connectivity at all places. Finally, one potential solution worth investigating further to provide more mature results is the use of VLC for V2X [54] and THz communications [55]. The solutions can provide significant segments of unused spectrum, but current technological obstacles need to be bypassed to render them a viable solution for V2X communications.
# PUF and Chaotic Map-Based Authentication Protocol for Underwater Acoustic Networks
## 2 Preliminaries
### Threat Model  
The proposed protocol adopted the widely accepted Dolev-Yao threat model (DY model) [32], in which any adversary has the ability to eavesdrop, intercept, modify, or delete the messages transmitted among users, gateways, and sensors. In addition, any adversary can extract all the sensitive information stored in the lost/stolen smart card of a legal user, \(U_{i}\), using the side channel attack. Meanwhile, any adversary can capture the gateway and sensor nodes.
## 4 Results and Discussion  
### Experimental Setup  
#### 4.1.1 Hardware and Software Environment  
In this study, to ensure efficient training and accurate testing of the model, we selected a computer cluster equipped with high-performance GPUs as the hardware platform. This platform is equipped with multiple NVIDIA Tesla V100 GPUs, Hongkong, China, each with 32 GB of memory, capable of providing up to 100 TeraFLOPS of single-precision floating-point computational power, which is essential for processing large-scale deep learning models and complex datasets. Additionally, the cluster includes several servers with high-speed CPUs and large memory capacities, each equipped with at least 64-core Intel Xeon processors, Hongkong, China and 512 GB of RAM, which helps provide ample computational resources during data preprocessing and model validation phases.  
On the software side, we primarily used the deep learning frameworks TensorFlow 2.18.0 and PyTorch 1.8. TensorFlow, developed by Google, is favored by many researchers and developers for its flexible computational graph concept, robust scalability, and broad community support. It supports various platforms and languages, allowing for easy deployment on different hardware. PyTorch, developed by Facebook's AI research team, is popular for its native support for dynamic computational graphs and its straightforward user interface. Its ease of use and modular design make it particularly suitable for rapid prototyping in research and development.  
To enhance the efficiency and reproducibility of experiments, we also utilized acceleration libraries such as CUDA and cuDNN to optimize GPU computational performance. These libraries significantly speed up deep learning computations, especially when performing intensive calculations on large datasets. Furthermore, to manage and monitor the experimental process, we deployed machine learning experiment management tools like MLflow, which can track experiments, record parameters, and manage the lifecycle of models.
Sensors_**2012**, _12_, 11754-11781; doi:10.3390/s120911754  
_Sensors_  
**ISSN 1424-8220**  
www.mdpi.com/journal/sensors  
_Article_  
Formal Specification and Validation of a Hybrid Connectivity Restoration Algorithm for Wireless Sensor and Actor Networks +
Footnote †: Abstract:** Maintaining inter-actor connectivity is extremely crucial in mission-critical applications of Wireless Sensor and Actor Networks (WSANs), as actors have to quickly plan optimal coordinated responses to detected events. Failure of a critical actor partitions the inter-actor network into disjoint segments besides leaving a coverage hole, and thus hinders the network operation. This paper presents a **P**artitioning detection and **C**onnectivity **R**estoration (PCR) algorithm to tolerate critical actor failure. As part of pre-failure planning, PCR determines critical/non-critical actors based on localized information and designates each critical node with an appropriate backup (preferably non-critical). The pre-designated backup detects the failure of its primary actor and initiates a post-failure recovery process that may involve coordinated multi-actor relocation. To prove the correctness, we construct a formal specification of PCR using Z notation. We model WSAN topology as a dynamic graph and transform PCR to corresponding formal specification using Z notation. Formal specification is analyzed and validated using the Z Eves tool. Moreover, we simulate the specification to quantitatively analyze the efficiency of PCR. Simulation results confirm the effectiveness of PCR and the results shown that it outperforms contemporary schemes found in the literature.  
**Keywords:** WSANs; connectivity restoration; controlled and coordinated actor relocation; formal specification; Z notation
# Train-Induced Unsteady Airflow in a Metro Tunnel with a Ventilation Shaft
## 2 Theoretical Analysis  
### The Premise of Theoretical Analysis  
In this study, we consider a long, straight tunnel containing a ventilation shaft, where the tunnel and shaft share the same wall roughness, with no slope or cross-sectional changes, as shown in Figure 1. Correspondingly, tunnels without a ventilation shaft of the same length are also considered for comparison. As the speed of the piston wind caused by the train movement is much less than the speed of sound, and because the Mach number is significantly small, the piston wind flow can be considered to be one-dimensional, unstable, and incompressible [14; 30]. Furthermore, when the train passes through the tunnel with a ventilation shaft, the airflow in the shaft is mainly affected by the operation of the train. The air temperature in the tunnel and shaft was considered to be the same as the ambient temperature. The stack effect of the shaft was ignored here, that is, the pressure difference between the shaft bottom and top and the tunnel entrance and exit was assumed to be zero. Based on the above assumptions and [PERSON]'s principle for unsteady flow, the unsteady airflow induced by the metro train was theoretically modeled and analyzed. Table 1 summarizes the physical quantities and their symbols used in the theoretical model. It is important to recognize that the application of the Bernoulli equation in theoretical modeling rests on certain assumptions. Consequently, employing the Bernoulli equation theory is appropriate for scenarios involving relatively simple tunnel structures; however, it becomes challenging to develop a theoretical model for more complex tunnel configurations. To distinguish different scenarios, piston wind (PW) caused by the train in a tunnel with a ventilation shaft is known as PW-WVS. Similarly, piston wind caused by the train in a tunnel without a ventilation shaft is known as PW-NVS.  
\begin{table}
\begin{tabular}{c l} \hline \hline
**Symbol** & **Physical Meaning** \\ \hline \(A_{T}\) & Cross-sectional area of the metro train (m\({}^{2}\)) \\ \(A_{L}\) & Cross-sectional area of the tunnel (m\({}^{2}\)) \\ \(A_{s}\) & Cross-sectional area of the ventilation shaft (m\({}^{2}\)) \\ \(H_{s}\) & Height of the air shaft (m) \\ \(V_{T}\) & Velocity of the train running at a constant speed (m/s) \\ \(V_{t}\) & Velocity of the train (variable) (m/s) \\ \(v\) & Velocity of the piston wind (m/s) \\ \(v_{u}\) & Velocity of the piston wind upstream of the tunnel (m/s) \\ \(v_{d}\) & Velocity of the piston wind downstream of the tunnel (m/s) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Physical quantities and their symbols are used in the theoretical model.  
Figure 1: Typical Tunnel Diagram.
# A Charging and Discharging Data Privacy Protection Scheme for V2G Networks Based on Cloud-Fog-End
## 4 Correctness and Security Analysis
### Security Analysis
#### 4.2.5 Resistance to Impersonation Attacks  
Through aggregate verification, the FN/CSO can confirm the legitimacy of the sender CP/FN's identity, making it impossible for attackers to forge the sender's legitimate signature or impersonate the sender's valid identity.