esa-sceva/satcom-chunk-collection huggingface dataset:
# Security Audit of a Blockchain-Based Industrial Application Platform
## 5 Discussion: Application of BISS:4.0 to Real-World Scenarios
### Scenario 4: Unwitting Disclosure of Confidential Data
* _Intention_: this attack scenario describes an attack that has already occurred in the real world with widespread impact [64] and could have been averted or mitigated through the use of the BISS:4.0 platform. Although this attack does not fit into the identified attack categories, this attack demonstrates the scope of protection offered by BISS:4.0. Although this was not a targeted attack, \"just a case of carelessness\", it clearly demonstrates how easily a malicious insider or external attacker can grab a company's secret data through an official data channel without them even realizing it.
* _Attack path and impact_: in this real-world scenario, a robotics manufacturer unknowingly disclosed confidential data (assembly line layouts, employee data, non-disclosure agreements, etc.) from a number of large manufacturing companies through its data exchange infrastructure.
* _Countermeasure_: in an earlier paper [65], a blockchain-based approach that creates and manages an audit trail of data exchange through the blockchain without requiring the data to be sent through the blockchain for the privacy validation was described. Instead, the confidentiality of the data was validated by a privacy validation module and the validated data was then exchanged between the companies involved in the data exchange via P2P communications. To ensure the trust and effectiveness of the modules, they were transparently validated and certified to users before use. In addition, a method for processing confidential data with smart contracts that trigger specific actions based on inputs without disclosing the data was described. Hash-based mapping was used to assign the confidential data to hash values, which were then compared against predefined trigger conditions that then triggered a specific action (e.g., triggering a maintenance task). This approach, which validates outbound data against a privacy policy before it is sent, prevents, or at least makes it more difficult for a malicious insider to leak the company's secret data through an official data channel.
## 2 Theory  
### A First Order Fit Using Gaussian Brackets  
As paraxial ray tracing resembles the optical system, the characteristics of the optical system can be calculated easily and quickly [14]. In this case, the effective focal length (EFL), back focal length (BFL), and front focal length (FFL) of the optical system can be expressed by Equations (1)-(3) using a Gaussian bracket [15]. Here, \(k\) represents the refracting power of the lens surface, \(d\) represents the distance between the front and rear surfaces of the lens, \(i\) represents the refractive index, and the  
Figure 1: Layout of (**a**) inner focus system [12] and (**b**) floating system [13].  
subscript is a number corresponding to each side of the optical system. From Equations (1)-(3), it is evident that the three variables must be adjusted to satisfy the desired \(EFL\), \(BFL\), and \(FFL\)[16].  
\[\left[k_{1},-\frac{p_{1}}{i_{1}}k_{2},-\frac{p_{2}}{i_{2}},\cdots,k_{i-1},-\frac {p_{i-1}}{i_{i-1}},k_{i}\right]=EFL, \tag{1}\]  
\[\left[k_{1},-\frac{p_{1}}{i_{1}}k_{2},-\frac{p_{2}}{i_{2}},\cdots,k_{i-1},- \frac{p_{i-1}}{i_{i-1}}\right]=BFL, \tag{2}\]  
\[\left[-\frac{p_{1}}{i_{1}}k_{2},-\frac{p_{2}}{i_{2}},\cdots,k_{i-1},-\frac{p_ {i-1}}{i_{i-1}},k_{i}\right]=FFL. \tag{3}\]  
For example, the focal length EFL of an optical system consisting of n elements is expressed by the Gaussian bracket method and the third, sixth, and tenth elements, \(\alpha_{3}\), \(\alpha_{6}\), and \(\alpha_{10}\), respectively, are extracted as indicated in Equation (4).  
\[EFL=\alpha_{1}a_{i}a_{j}a_{k}+\alpha_{2}a_{i}a_{j}+\alpha_{3}a_{j}a_{k}+\alpha _{4}a_{i}a_{k}+\alpha_{5}a_{i}+\alpha_{6}a_{j}+\alpha_{7}a_{k}+\alpha_{8}, \tag{4}\]  
\[BFL=\beta_{1}a_{i}a_{j}a_{k}+\beta_{2}a_{i}a_{j}+\beta_{3}a_{j}a_{k}+\beta_{4} a_{i}a_{k}+\beta_{5}a_{i}+\beta_{6}a_{j}+\beta_{7}a_{k}+\beta_{8}, \tag{5}\]  
\[FFL=\gamma_{1}a_{i}a_{j}a_{k}+\gamma_{2}a_{i}a_{j}+\gamma_{3}a_{j}a_{k}+\gamma _{4}a_{i}a_{k}+\gamma_{5}a_{i}+\gamma_{6}a_{j}+\gamma_{7}a_{k}+\gamma_{8}, \tag{6}\]  
\[S_{1}{a_{k}}^{4}+S_{2}{a_{k}}^{3}+S_{3}{a_{k}}^{2}+S_{4}a_{k}+S_{5}=0. \tag{7}\]  
Here, element a represents the selected variable, refractivity or thickness, and the subscripts \(i,j,k\) represent the first, second, and third variables in order, and \(\alpha\), including the subscript, is defined to simplify the expansion of the Gaussian parenthesis [15]. By defining \(\beta\) and \(\gamma\) in the same way, BFL and FFL can be expressed simply as Equations (5) and (6). In order to obtain each variable, Equations (4)-(6) are combined and expressed as Equation (7), where \(S_{1}\), \(S_{2}\), \(\cdots\)\(S_{5}\) denote the coefficients of the quadratic equation. Therefore, by obtaining the solution of Equation (7), it is possible to obtain design variables \(\alpha_{i}\), \(\alpha_{j}\), and \(\alpha_{k}\) satisfying an arbitrary focal length, back focal distance, and front focal length.
# Dynamic Multi-Attribute Decision-Making Method for Risk-Based Ship Design
## 5 Example Application  
### Case Background  
The IMO conducted an FSA of oil tankers at a high level in the IMO FSA report. In step 1 and step 2 of this FSA, collision, grounding, fire, and blast contacts were identified as the main accident hazards, and the corresponding risks (accident probability and consequence) were analyzed quantitatively. In step 3, eight types of RCOs are proposed by an expert group to control accident risks and are listed in Table 1. The cost-benefit analysis was conducted using the GCAF index, and the analysis results were compared with the improved grey correlation degree method to demonstrate the reliability of the new method when considering more attribute indicators.  
In Table 2, all the index data are the average attribute level per ship in its lifetime (25 years). Because the attribute data of certain options do not exist (as presented in Table 2), the most conservative information-processing method is used to complement them. In risk assessment, if some attribute data are missing, the most conservative information processing method can be used to supplement it in order to avoid underestimating the risk. This approach maximizes the risk estimate, thereby better protecting interests and safety. It helps to prevent potential risks to the greatest extent possible, avoiding the underestimation of risks due to missing data, which could lead to accidents or losses. This conservative decision-making enables more cautious actions to be taken in the face of uncertainty and risk, thereby reducing potential risks and losses. For example, the \(\triangle\)PLL data of RCO3 do not exist, and therefore, the minimum data in the \(\triangle\)PLL sequence (0.00012) are used to complement it; the CATS data of RCO7 do not exist, and thus, the maximum data in the CATS sequence (320) are used to complement it.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**RCO** & **Accident Risks** \\ \hline RCO1 & Active steering gear redundancy \\ RCO2 & Electronic chart display and information \\ RCO3 & Terminal proximity and speed sensors \\ RCO4 & Navigational sonar \\ RCO5 & Better implementation of hot work \\ RCO6 & Double-walled hot fuel lines in the engine \\ RCO7 & Engine control room additional emergency \\ RCO8 & Hull stress and fatigue monitoring system \\ \hline \hline \end{tabular}
\end{table}
Table 1: RCOs for accident risks of crude oil tankers.
## 4 Simulation and Results Analysis
### Elastic Resource Allocation Evaluation  
This section conducts a simulation and analysis of the DTG-LFA algorithm proposed in this article. We select two comparative algorithms: the KSP-FF algorithm and the MTLSC algorithm [34]. We assign candidates resource blocks with the maximum consecutiveness to the request. The consecutiveness is defined as \((\sum_{i=1}^{L-1}u_{i}\cdot u_{i+1})/B)*(\sum_{i=1}^{L}u_{i}/L)\), where \(B\) is the number of resource fragmentations and \(L\) is the number of slots on each wavelength.  
From Figure 9a, the DTG-LFA algorithm has the lowest blocking probability as the traffic load increases. Using the maximum traffic intensity as an example, the blocking probabilities of the KSP-FF algorithm, MTLSC algorithm, and DTG-LFA algorithm are 0.375, 0.068, and 0.043, respectively. Figure 9b shows that the utilization of the wavelength of the DTG-LFA algorithm is between the KSP-FF algorithm and the MTLSC algorithm. As the traffic load increases, the trend of wavelength utilization in descending order is the KSP-FF algorithm, MTLSC algorithm, and DTG-LFA algorithm. Because the DTG-LFA algorithm comprehensively evaluates the candidate resource blocks, makes full use of resources, and reduces resource fragments so that more requests can be transmitted, the blocking probability of requests is reduced.  
Figure 8: EOSNV-ETL-PE algorithm performance vs. Traffic load. (**a**) Blocking probability vs. traffic load. (**b**) Wavelength utilization vs. traffic load.
# Engineering Design of the European DEMO HCPB Breeding Blanket Breader Zone Mockup
## 2 Design of the Mockup
### Dipscaling  
The HELOKA facility offers a suitable DEMO-relevant testing range. HELOKA operates at temperatures ranging from room temperature to 550 \({}^{\circ}\)C, pressure ranging from 4 to 9.2 MPa, mass flow rate ranging from 20 g/s to 1400 g/s [15]. The pressurized helium coolant of the HCPB blanket has the nominal inlet/outlet of 300/520 \({}^{\circ}\)C. The mass flow rate per fuel-breeder pin in the breeder zone of HCPB blanket ranges from about 15 g/s to 23 g/s. To facilitate the measurement of temperature and controlling of mass flow rates, upscaling is needed, in which the same range of Reynolds number (Re), Peclet number (Pe), and the dimensionless heating rate q\({}^{+}\)[16] are kept. Pe is equal to the multiplication of Re to Prandtl number (Pr). The Pr of helium is almost constant, therefore, only Re and q\({}^{+}\) are the relevant parameters for upscaling. The definition of q\({}^{+}\) is shown in Equation (5) where \(A_{cross}\) is the cross-section area of the channel, Q is the heating power, \(A_{heat}\) is the heating surface area.  
\[q^{+}=\frac{Q}{\dot{m}*c_{p}*T_{inlet}}\frac{A_{cross}}{A_{heat}}=\frac{q_{w}^ {\prime\prime}}{(\dot{m}/A_{cross})*c_{p}*T_{inlet}} \tag{5}\]  
Figure 2: Annular channel.  
The nominal Re number in the annular channel of the HCPB blanket breeder zone ranges from 4000 to 5900. A wider range of Re (4000, 6000, 8000, and 10,000) was planned to cover any fluctuation in the mass flow rate in the HCPB blanket. The maximum heat flux from the ceramic breeder side to the annular coolant is about 10 times higher than that from Be\({}_{12}\)Ti block. Therefore, in the mockup only the inner side of the annular channel is heated to ease manufacturing and assembly efforts. The dimensionless heating rate, q\({}^{+}\), of the HCPB breeder zone from ceramic breeder side to coolant in the annular channel ranges from 0.0002 via 0.0003, 0.0004, and 0.0005 to 0.00058. Therefore, the proposed q\({}^{+}\) in this experiment are 0.0002, 0.0003, 0.0004, 0.0005, and 0.0006. Here, a comparison table of the heat flux at the same q\({}^{+}\) at a selected Re number (Re = 4000, 21 g/s in HCPB and 25 g/s in mockup) is shown in Table 1.
# Non-Intrusive Load Disaggregation Based on a Multi-Scale Attention Residual Network
## 2 Multi-Scale Attention Residual Network
### Attention Mechanism  
A convolution kernel, the core of a CNN, is generally regarded as aggregating information spatially and channel-wise on local receptive fields. A CNN is composed of a series of convolution layers, non-linear layers, and down-sampling layers, among others, and captures required features from global receptive fields [30].  
However, in order to obtain better network performance, a squeeze and excitation attention mechanism is used in the network [31]. Its structure is shown in Figure 2. One of the novelties of the algorithm in this paper lies in the use of the attention mechanism in the residual structure to further improve the feature extraction ability of the network, especially for the extraction of electrical features that are not frequently used. This attention mechanism is different from the previous structure, as it improves performance from feature channels. The first 3D matrix in Figure 2 is composed of a feature graph \(C\) with a size of \(H\times W\).  
Figure 1: The residual structure.  
According to Figure 2, the spliced feature map of a multi-scale module is obtained through the global pooling layer to obtain attention vector \(z_{c}\), which is compressed from the spatial dimension to obtain the global receptive field. \(z_{c}\) is a high-dimensional vector containing the low-order global feature information of the feature map, and its dimension is \(1\times 1\times C\). The expression equation is as follows:  
\[z_{c}=F_{sq}(u_{c})=\frac{1}{h\times w}(\sum_{h}^{i=1}\sum_{w}^{j=1}u_{c}(i,j)). \tag{6}\]  
Next, two fully connected layers are applied to establish correlations between channels as excitation and to output the same number of weights as the input feature. The equation is  
\[s_{c}=F_{ex}(z_{c},w_{2})=\delta(w_{2}\sigma(w_{1}z_{c}), \tag{7}\]  
where the dimensions of \(w_{1}\) and \(w_{2}\) are all \(\frac{C}{s}\times C\), s represents the scaling coefficient, and the attention vector \(s_{c}\) is obtained after being activated by the Relu function and the Sigmoid function. It is a high-dimensional vector of high-order global feature information obtained on the basis of attention vector \(z_{c}\). The attention vector \(s_{c}\) further represents the change of load feature information in the channel dimension, and its dimension is also \(1\times 1\times C\). A Sigmoid activation function is similar to a gating processing mechanism, which generates different weights for each feature channel of the attention vector \(s_{c}\). Finally, the original three-dimensional matrix \(u_{c}\) is multiplied to complete the weight recalibration. Therefore, the importance of each load feature is obtained according to the feature map [32]. Finally, the useful load characteristics are enhanced and the less useful load characteristics are suppressed, which can improve the accuracy of load disaggregation of low-usage appliances. The equation is  
\[X=F_{scale}(u_{c},s_{c})=u_{c}\cdot s_{c}. \tag{8}\]
# System of Systems Lifecycle Management--A New Concept Based on Process Engineering Methodologies
## 7 Introducing the System of Systems Lifecycle Management Model
### Continuous Enhancement, Improvement, and Risk Management  
The circular model illustrates well that this is a continuous development strategy, where the information gathered during the cycles can serve as useful inputs for the management of further applications or even the enhancement and improvement of the current application. During the whole lifecycle, based on the gathered information and feedback, the risks are also taken into account; therefore, they can be continuously treated.
# A Privacy-Preserving Record Linkage Method Based on Secret Sharing and Blockchain
## 1 Introduction
Only a single interaction involving the sharing of masked differences is required between nodes, significantly reducing the communication and computational overhead compared to computationally intensive approaches like those based on homomorphic encryption and effectively enhancing resilience against collusion attacks. 2. Addressing the trust issues of third parties and participants in existing PPRL methods, we utilize blockchain smart contracts to manage the entire PPRL process (including verifiable mask generation, task allocation, and node management) and enable trusted auditing. Combined with IPFS for efficient data storage and distribution, the use of tamper-proof records and automated rule execution enhances process transparency and the trustworthiness of computation nodes. 3. Through theoretical analysis and experimental validation, we demonstrate that this method ensures data privacy and security while achieving high linkage quality and good scalability, making it suitable for large-scale multi-party collaboration scenarios. The structure of this paper is organized as follows: Section 2 reviews related work, analyzing existing technologies in the PPRL domain and their limitations. Section 3 in troduces the background knowledge and techniques involved in this method. Section 4 elaborates on the proposed PPRL method based on secret sharing and blockchain. Section 5 analyzes the method's security, computational complexity, and resilience against collusion attacks. Section 6 validates the effectiveness of the method through experiments. Section 7 summarizes the research findings and discusses future directions.
## 2 Theoretical Background
### GRU Network  
GRU, a variant of RNN, introduces a unique gating mechanism built on top of the standard RNN architecture [29]. Compared to LSTM networks, GRU uses only two gates, which results in fewer model parameters and faster inference speeds. The gating mechanism controls the flow of information within the recurrent unit, allowing the network to selectively forget irrelevant historical information while retaining key features, thus enabling better capture of the temporal trends in sequential data [30]. As shown in Figure 4, the GRU structure consists of two gates: the reset gate and the update gate. The internal computation of GRU [31] is given by the following equations:  
Reset gate:  
\[z_{t}=\sigma(w_{z}\cdot[h_{t-1},x_{t}]) \tag{9}\]  
Update gate:  
\[r_{t}=\sigma(w_{r}\cdot[h_{t-1},x_{t}]) \tag{10}\]  
Figure 4: Structure diagram of GRU.  
Candidate hidden layer state:  
\[\widetilde{h}_{t}=\tanh(w_{i}\cdot[r_{t}\cdot h_{t-1},x_{t}]) \tag{11}\]  
Hidden layer state:  
\[h_{t}=z_{t}\cdot\widetilde{h}_{t}+(1-z_{t})\cdot h_{t-1} \tag{12}\]  
where \(h_{t-1}\), \(\widetilde{h}_{t}\), and \(h_{t}\) represent the hidden state at the previous time step, the candidate hidden state at the current time step, and the hidden state at the current time step, respectively. \(x_{t}\) is the input at time step t; \(w_{z}\), \(w_{r}\), and \(w_{i}\) are the weights for computing the update gate, reset gate, and candidate hidden state, respectively. \(z_{t}\) and \(r_{t}\) denote the values of the update gate and reset gate. \(\sigma(\bullet)\) is the sigmoid activation function, and \(\tanh(\bullet)\) is the hyperbolic tangent function.  
The outputs of the reset gate and update gate are determined by the hidden state from the previous time step and the input at the current time step, with values ranging between 0 and 1. The reset gate controls the extent to which the previous hidden state contributes to the candidate hidden state at the current time step, while the update gate determines how much of the previous hidden state is retained in the current hidden state.
## 3 Method
### Feature Concatenation and Relation Classification  
The output features of the cascaded word vector attention were used as the input features for the relation classification task. The cascaded word vector attention has three outputs, which are the feature vectors corresponding to Entity 1, Entity 2, and the \([CLS]\) tokens. The purpose of the relationship classification task is to identify the relationship between two entities, so the feature vectors corresponding to the two entities are more important. Because the relationship classification network extracts the features between two entities, only by acquiring entity features can the relationship between two entities be learned and judged. At the same time, the characteristics of sentences in which two entities exist at the same time are also very important, because the sentence feature not only contains two entities, but also contains the context information between the two entities. With contextual features, the model can better learn the relationship between two entities, thus improving the performance of the relation classification network.  
By concatenating Entity 1, Entity 2, and the \([CLS]\) tokens, the total features of relation classification are formed. The total feature contains rich relational features between two entities and also contains contextual information in the \([CLS]\) tags. The feature dimension is mapped to the number of relations through a fully connected layer. Using the output probability after the fully connected layer as the basis for relation classification, the above process can be expressed in mathematical form as Equation (20).  
\[P=softmax\big{(}concat(e_{1},e_{2},e_{[CLS]})\big{)} \tag{20}\]  
Among them, \(P\) is the probability of each relationship output by the fully connected layer. concat represents the splicing operation. \(e_{1}\), \(e_{2}\), and \(e_{[CLS]}\) denote feature vectors that have undergone concatenated word vector attention, respectively.
# Temporal Convolutional Neural Network for the Classification of Satellite Image Time Series
## 3 Material and Methods
### Benchmark Algorithms
#### 3.4.2 Recurrent Neural Networks  
First developed for sequential data, RNN models have been recently applied to several classification tasks in remote sensing, and particularly for crop mapping [45; 46; 48]. They share the learnt features across different positions. As the error is back-propagated at each time step, the computational cost can be high and it might cause learning issues such as vanishing gradient. Hence, most recent RNN architectures use LSTM or GRU units that help to capture long distance connections and solve the vanishing gradient issue. They are composed of a memory cell as well as update and reset gates to decide how much new information to add and how much past information to forget. Moreover, RNNs are known to be able to handle series with different lengths, but to the best of our knowledge, not about dealing with an irregular sampling within one sequence.  
Following the most recent studies, we train bidirectional RNNs with stacks of three GRUs, one dense layer with 256 neurons and a Softmax layer [46; 48]. The same number of neurons is used in the three GRUs. More specifically, we have trained five different models comprised of 16, 32, 64, 128 or 256 neurons. As we focus on the analysis of TempCNNs in this paper, Section 4.1 will report only the best RNN result (128 neurons) obtained on the test instances. All the models have been trained similarly to the CNNs with Keras (backend Tensorflow): batch size equal to 32, Adam optimization, and monitoring of the validation loss with an early stopping mechanism (c.f., Section 2.3).