esa-sceva/satcom-chunk-collection huggingface dataset:
# Information Fusion in Autonomous Vehicle Using Artificial Neural Group Key Synchronization
## 3 Proposed Methodology
### Synchronization Using B-Tree Framework
Step 4: When the findings of two TLTPMs \(G\) and \(D\) agree, \(\zeta^{G}=\zeta^{D}\) adjusts the weights using one of the following rules: Both TLTPMs will be learned from each other using the Hebbian learning method (Equation (7)). \[\delta^{q+}_{v,w}=fun\left(\delta^{q}_{v,w}+\gamma^{q}_{v,w}\zeta\Phi\left( \xi^{q}_{v}\zeta\right)\Phi\left(\zeta^{G}\zeta^{D}\right)\right)\] (7) If the weights for each TLTPM are the same, go to step 6. Step 5: If the outcomes of the both TLTPMs differ, the weights cannot be altered, \(\zeta^{G}\
eq\zeta^{D}\). Go to step 2 now. Step 6: As cryptographic keys, use these coordinated weights. ```  
**Algorithm 1**The whole TLTPM sync mechanism. If the procedure manages the joining and leaving of individuals from the group as below. 1. Leaving from the grouping Figure 9 depicts a situation in which a member \(USER\)\(j\), let \((USER\)\(6)\), wishes to quit the group at level 2 after obtaining keys \((USER\)\(1234)\) and \((USER\)\(5678)\). It is now apparent that omitting \((USER\)\(6)\) will not result in a change to \((USER\)\(1234)\) obtained from the pair \((USER\)\(12\)\(USER\)\(34)\). As a result, these users do not need to execute their TLTPM once again to generate coordinated weight vector. The weights of \((USER\)\(56)\) impacts the \((USER\)\(5678)\), although \((USER\)\(78)\) can also keep their coordinated weight vector from level 1. That \((USER\)\(5)\) is a single node, its TLTPM will have initial weight vector and will coordinate with \((USER\)\(78)\) to obtain a pair of coordinated keys for \((USER\)\(578)\). After that, \((USER\)\(1234)\) and \((USER\)\(578)\) execute their separate TLTPMs to merge to the same weight vector, that becomes the group of principals' neural key. 2. Joining to the group If any user \(USER_{j}\) wishes to participate in the group, such as a fresh member\((USER\)\(9)\), \((USER\)\(9)\) get linked with the root of the tree, as illustrated in Figure 10. Previously, with the eight members, two levels are needed to construct the key, namely \((USER\)\(1,\)\(USER\)\(2)\)\((USER\)\(3,\)\(USER\)\(4)\)\((USER\)\)\((5,\)\(USER\)\(6)\) and \((USER\)\(7,\)\(USER\)\(8)\) instruct their networks in simultaneously at 0 th, and then in 1 st level \((USER\)\(12,\)\(USER\)\(34)\) and \((USER\)\(56,\)\(USER\)\(78)\) will instruct their networks for synchronization, and in level 2 \((USER\)\(1234,\)\(USER\)\(5678)\) will instruct their TLTPM's and merge to an identical weight vector. Now, there are 9 participants in the group \((USER\)\(1,\)\(USER\)\(2,\)\(USER\)\(3,\)\(USER\)\(4,\)\(\ldots
# Cache Servers Placement Based on Important Switches for SDN-Based ICN
## 1 Introduction
The recent approaches proposed for centralized cache management in SDN-based ICN do not place the cache servers efficiently as follows. The authors in [8] deploy the cache servers based on minimum path-stretch values. However, it produces more traffic overhead. To reduce the traffic overhead, [PERSON] et al. [28] suggest deploying the cache servers by considering higher closeness centrality, minimum path-stretch values, and higher betweenness centrality of the network. However, when the number cache servers get increased, the time taken to compute the location of cache servers using the values of closeness centrality, path-stretch, and betweenness centrality is very computationally extensive due to the NP (non-polynomial) nature of the problem. We observe that the computation time of this approach, i.e., the existing one in [28], for two cache servers and three cache serves are about 9 h and 1 month, respectively, in the topology of 415 switches using the real network traces ([[https://github.com/fg-inet/panoptisim](https://github.com/fg-inet/panoptisim)]([https://github.com/fg-inet/panoptisim](https://github.com/fg-inet/panoptisim)) Accessed on 4 July 2019) on the desktop computer of Intel CPU i3 which runs Fedora Linux 23. We solve this problem by using the concept of important switches as follows. Through experimental results, the researchers show that the traffic matrices in the networks are approximately low-rank [30], i.e., the traffic matrices are sparse. This phenomenon means that most of the data flows are passing through only a few switches/routes. We call these switches as important switches in the network. The sparse matrices can be solved by using singular-value-decomposition (SVD) and QR factorization with column pivoting techniques of linear algebra [31]. We describe in detail these steps in Section 3.2. Our proposed approach reduces the computation time to a few seconds for our topology of 415 switches. After computing the important switches, this paper proposes to place the cache servers on the most important switches according to the number of required cache servers. Through simulation on the real network traces, the results show that this approach reduces not only the computation time, but also performs better than the existing approach [28] in terms of traffic overhead in the network. We organize the rest of the paper as follows. We discuss the related work in Section 2. Section 3 explains the proposed approach in detail. The results are discussed in Section 4. Finally, Section 5 concludes the paper along with future research directions.
###### Abstract  
The real-time compaction quality evaluation of earth-rock dam plays a pivotal role in ensuring dam safety. However, the current real-time compaction quality evaluation only takes the physical properties of compacted dam materials into account, which fails to characterize whether their mechanical property meets the requirements of deformation and destruction, and no quantitative heterogeneity of real-time compaction quality is studied. This paper presents a comprehensive evaluation method to address these problems. First, based on on-site tests, real-time physical and mechanical indices are obtained. Next, the analytic hierarchy process, extended by the interval model (_i_-AHP) method, is introduced for real-time compaction quality evaluation considering both these indices, and the hybrid compaction index (_HCI_) is firstly proposed based on the _i_-AHP method. Finally, an improved geostatistical analysis method (_i_-GAM) is developed to quantify the real-time compaction quality heterogeneity. A case study of an earth-rock dam project in southwest China demonstrates the effectiveness and advantages of the proposed method.  
compaction quality evaluation; earth-rock dam; physical and mechanical properties; _i_-AHP method; improved geostatistical analysis method (_i_-GAM); real-time compaction monitoring (RTCM) +
Footnote †: journal: Applied Sciences
# Physics-Informed Surrogate Modelling in Fire Safety Engineering: A Systematic Review
## 2 Materials and Methods
### Discussion and Conclusions  
The preceding literature review has explored the expanding field of PISM within fire safety engineering, showcasing its diverse applications across fire dynamics, wildfire behaviour, structural fire engineering, material degradation, and heat transfer. Notably, although there are multiple PISM applications across most fire safety domains, researchers predominantly favour data-driven models, employing them as purely mathematical tools for prediction without explicit incorporation of physical principles. The heat transfer field stands as a notable exception, where physics-informed approaches are increasingly prevalent, thanks to the clearly defined mathematical formulations (PDEs) in this subdomain, allowing for direct implementation through LCT.  
Our observations indicate that the practical application of PISM varies significantly. This highlights the importance of tailoring modelling strategies to the specific characteristics of each problem. We have learned that effective integration of physical principles can enhance model accuracy (as in [97]), but also that challenges persist. This is especially the case in areas where specifying the underlying physics is hard, such as in the field of concrete spalling, where all identified studies so far remain data-driven. Furthermore, the review highlights that adopting a single strategy, particularly feature engineering (FET) and offline-constrained techniques (OCT), does not guarantee a fully physics-compliant model. Combining multiple strategies [14, 97, 100, 136] is therefore recommended. Additionally, PISM models often exhibit more challenging convergence during training compared to purely data-driven models, especially under severe and realistic fire conditions.  
A final critical observation is that model limitations, such as physical constraint violations or poor generalisation under extrapolative conditions, are rarely addressed in reported studies. This hampers the ability to identify best practices. Future research should prioritise addressing critical gaps, including uncertainty quantification, data efficiency, multi-physics integration, and explainable AI [40]. The development of standardised validation metrics and open-source libraries will also be crucial. By acknowledging these challenges and building upon the strengths of PISM, the promise of surrogate modelling techniques in FSE can be achieved, creating more reliable and insightful predictive tools with limited computational cost.
## 3 An Adaptive Attitude Estimation Method Based on Multi-Sensor Fusion
### Attitude Estimation Based on Kalman Filter
#### 3.3.1 Kalman Filter for Inertial Attitude Estimation  
Set state equation and measurement equations of random linear discrete system as:  
\[\left\{\begin{array}{l}x_{k}=A_{k,k-1}x_{k-1}+B_{k,k-1}u_{k}+\omega_{k}\\ z_{k}=H_{k}x_{k}+\
u_{k}\end{array}\right., \tag{10}\]  
where \(x_{k}\) is the state variable, \(z_{k}\) is the measurement variable, \(A_{k,k-1}\) is the non-singular state one step transition matrix, \(B_{k,k-1}\) is the process noise input matrix, \(u_{k}\) is the control input, \(\omega_{k}\) is the random process noise sequence, \(H_{k}\) is the measurement matrix and \(\
u_{k}\) is the measurement noise sequence.  
Time update equations:  
\[\hat{x}_{k}^{-}=A\hat{x}_{k-1}^{-}+B\hat{u}_{k-1}+\hat{\omega}_{k-1}, \tag{11}\]  
\[P_{k}^{-}=AP_{k-1}A^{T}+Q. \tag{12}\]  
State update equations:  
\[K_{k}=P_{k}^{-}H^{T}(HP_{k}^{-}H^{T}+R)^{-1}, \tag{13}\]  
\[\hat{x}_{k}=\hat{x}_{k}^{-}+K_{k}\big{(}z_{k}-H\hat{x}_{k}^{-}\big{)}, \tag{14}\]  
\[P_{k}=(I-K_{k}H)P_{k}^{-}. \tag{15}\]
## 2 Related Work  
### Language Bias in VQA  
The language bias [8] in VQA has a negative impact on the general application of the model in real-world scenarios. The reason behind it is that there is often a strong correlation between questions and answers. Moreover, the questions tend to concern conspicuous objects in the image. In VQA v1 [1] and v2 [7], a positive answer or a question-related answer tends to have higher accuracy. When the questions and answers in the training set and the test set are distributed inconsistently, this language bias is obvious. Therefore, the VQA-CP v2 dataset was recently proposed to evaluate the language bias.  
Figure 2: The VQA-CP v2 dataset contains images with multi-view (features). The authors of this paper visualize the features at the same layer in the neural network. For the same question, although the images, views and features are different, the semantic information is the same. Broadly speaking, this “multi-view” structure [12] exists both in the original data and the feature sets extracted from the middle layer.  
Train and test splits of the VQA-CP v2 have different question-answer distributions. The current approach to language bias can be divided into (1) Strengthening visual information: AttAlign [24], HINT [24], SCR [25], ReGAT [26], ESR [27], VGQE [28] and so on; (2) Weakening language priors: AdvReg [29], GRL [30], RUBi [31], LM [32], LMH [32], Bias-Product (POE) [32], RMFE [33], CF-VQA [34] and GGE-DQ [35]; (3) Using various data enhancement: CSS [36], CL-VQA [37], GradSup [38], Loss-Rescaling [39], Mutant [40], RandImg [41], Unshuffling [42], ADA-VQA [43] and X-GGM [44].
# Learning an Efficient Convolution Neural Network for Pansharpening
## 4 Experiment
### Experimental Results and Analysis
#### 4.2.3 Generalization  
The design of our model was intended to be more robust when generalized to different satellites, as the proposed model was relative shallow and several improvements have been made to further boost the generalization. To empirically show this, we retained the model trained on WorldView-2 and IKONOS datasets to merge images from WorldView-3 QuickBird directly. We show the visual results in Figures 10 and 11.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **QNR** & \(D_{\lambda}\) & \(D_{\text{S}}\) & **SAM** & **SCC** & **Time** \\ \hline
**Reference** & 1 & 0 & 0 & 0 & 1 & 0 \\
**BDSD** & 0.8609 & 0.0523 & 0.0916 & 3.9974 & 0.5944 & 0.24 s (CPU) \\
**NIHS** & 0.8566 & 0.0382 & 0.1094 & 2.1968 & 0.8098 & 2.99 s (CPU) \\
**Indusion** & 0.8359 & 0.0859 & 0.0855 & 1.9411 & 0.8112 & 0.16 s (CPU) \\
**NMRA** & 0.7453 & 0.1245 & 0.1486 & 1.8985 & 0.8196 & 0.50 s (CPU) \\
**\(\ell\)1/2** & 0.7813 & 0.0880 & 0.1423 & 1.8592 & 0.8083 & 12.89 s (CPU) \\
**PNN** & 0.8496 & 0.0434 & 0.1118 & 2.6279 & 0.8046 & 0.60 s (GPU) \\
**MSDCNN** & 0.8705 & 0.0397 & 0.0936 & 2.5754 & **0.8201** & 0.14 s (GPU) \\
**Proposed** & 0.9096 & 0.0197 & 0.0721 & 1.7561 & 0.8150 & 0.07 s (GPU) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Performance indictors at original scale on WorldView-2 dataset.  
Figure 10: Results of the reduced scale experiment on an area extracted from a QuickBird image. (**a**) LRMS; (**b**) BDSD; (**c**) NIHS; (**d**) Indusion; (**e**) NMRA; (**f**) \(\ell\)1/2; (**g**) PNN; (**h**) MSDCNN; (**i**) Proposed; (**j**) Ground Truth.  
As we can inspect from the fusion result, the proposed CNN model displayed stable performance with sharp edges and inconspicuous spectral distortions, while the other CNN models could not generalize well. Since PNN and MSDCNN neglected the effect of overfitting, MSDCNN in particular adopts a relative deep network with limited training samples, which makes the model less robust. For the remaining traditional algorithms, accordant conclusions as those of aforementioned experiments can be drawn from Figures 10 and 11: NIHS, Indusion, and NMRA introduce different levels of blurring artifacts, whereas \(\ell 1/2\) and BDSD suffer from serious spectral distortions.
# Decorrelation-Based Deep Learning for Bias Mitigation
## 4 Results  
### Simulated Dataset  
To validate the performance of our proposed approach DcCNN, we apply our method and baseline to simulated the dataset to mitigate bias planted in the dataset. The baseline model is trained with \(\lambda=0\) where decorrelation is not performed to mitigate the bias \(m_{2}\). Figure 4 shows tSNE plots of learned features for baseline as well as DcCNN models. The color bar indicates the value of bias variable \(m_{2}\). From the plots, we can see that there is a correlation between features and \(m_{2}\) for the baseline model whereas features learned by DcCNN have a roughly uniform distribution of features across all values of \(m_{2}\) indicating no dependency of features on bias \(m_{2}\). This indicates that our proposed DcCNN successfully mitigates the bias present in the dataset.  
We also plotted the decorrelation function against iterations in Figure 5 to compare the performance of models in regards to reducing the statistical dependence between features and bias variables. This figure shows the unsmoothed distance correlation values in light blue and light orange colors. In contrast, dark blue and dark orange colors indicate the smoothed distance correlation values which are calculated using exponential moving average. Smoothing is used to observe the overall trend. It shows that the distance correlation between features and bias variable decreases as the number of iterations increases for DcCNN as opposed to the baseline model.  
Figure 3: Colored MNIST Training and Testing Datasets Examples with color bias: (**a**) Some image examples of color-biased MNIST dataset - Modified MNIST dataset with a color bias for each digit. Taken from [6] (**b**) Some image examples of reversed color-biased MNIST dataset—Binary group based color bias prepared for IRM [26].
## 3 Materials and Methods
### 5G Implementation Requirements
* Network slicing for creating dedicated virtual networks to meet the specific requirements of different applications, improving network performance and security. * Edge computing for real-time processing and analysis of data at the edge of the network, reducing latency and improving the performance of time-sensitive applications. * Low latency for real-time communication and response, aiming to achieve a latency of less than 1 millisecond. * High reliability for mission-critical applications, ensuring high availability and low downtime. * Full duplex communication for simultaneous transmission and reception of data, improving efficiency, capacity, and reducing latency. The combination of these technologies in 5G networks can enable a wide range of applications, from IoT services to high-bandwidth applications, such as virtual reality and augmented reality. 5G networks aim to provide the necessary performance metrics to support these applications, enabling the growth of the IoT ecosystem and a new era of connectivity.
# Blockchain Technology for Intelligent Environments
## 5 Blockchains for IoT Systems
### Marketplace-Oriented Systems  
#### 5.3.1 IOTA Marketplace  
_IOTA Marketplace_[74] is a pilot application of the IOTA technology. IOTA marketplace is in essence a data marketplace where users can purchase access to a sensor data stream. It currently supports tens of sensor types, mainly provided by the IOTA consortium members. Payments are made using the IOTA currency.  
Sensor measurements are propagated in real time over the IOTA network using the _Masked Authenticated Messaging_ (_MAM_). MAM is a data communication protocol which adds functionality to emit and access an encrypted data stream, like RSS, over Tangle [75] (IOTA's distributed ledger), regardless of the size or cost of the device. IOTA's consensus protocol adds integrity to these message streams [76].  
Datum  
_Datum_[77] is a distributed marketplace for data monetization that leverages an Ethereum-based smart contract platform. The platform allows anyone to securely and anonymously store and trade structured data collected from social networks, wearables, smart homes, and other IoT devices.  
The Datum network claims to consist of 3 billion DAT tokens, with 1.53 billion available in a public token crowdsale. Up to 40% of raised funds is hedged in USD, EUR, and BTC. The Datum network aims to disrupt current data broker models by eliminating disintermediation in trading of social and IoT data.  
Datum encompasses the following key elements:  
* A decentralized data store allowing users to store structured data securely.
* The DAT token enabling payments for data storage and sharing.
* A data marketplace, enabling individuals to monetize their data on their terms.
* A mobile client application, available on Android and iOS.
* Datum leverages Ethereum, BigchainDB, and IPFS to provide a scalable, decentralized data storage backend.
* Data storage and data sharing is paid for by the DAT token. Data can be purchased as one-off or on an ongoing subscription basis. Interestingly, trading of DAT Tokens is prohibited for U.S. Citizens and residents in China and South Korea.
# Supervised Blockchain Anonymous Transaction Model Based on Certificateless Signcryption
## 3 Preliminaries
### Paillier Homomorphic Encryption  
The Paillier homomorphic encryption algorithm was proposed by [PERSON] [18] in 1999. It is an asymmetric encryption algorithm that allows for addition and multiplication operations to be performed directly on encrypted data without the need for decryption.  
It is based on the composite residuosity class problem's homomorphic scheme. Paillier algorithm mainly includes three parts: key generation, encryption and decryption.  
1. Key generation algorithm: Randomly select two large prime numbers p and q of equal length, and \(\gcd=(pq,(p-1)(q-1))=1\). Compute \(n=pq\) and \(\lambda=lcm(p-1,q-1)\), where \(\lambda\) represents the least common multiple. Randomly select the integer \(g=1+n\in Z_{n^{2}}\)*, where \(Z_{n^{2}}\)* \(Z_{n}\times{Z_{n}}\)*, \(Z_{n}=\{x|x\in Z,0\leq x\leq n\}\), \(Z_{n}^{*}=\{x|x\in Z,0\leq x\leq n,\gcd(x,n)=1\}\), so that \(\mu=\left(L(g^{3}\text{mod}n^{2})\right)^{-1}\text{mod}n\), the public key is \((n,g)\), the private key is \((\lambda,\mu)\), where \(L(x)=\frac{x-1}{n}\).
2. Encryption algorithm: Set plaintext to m, \(0\leq m\leq n\), select random numbers \(r\in Z_{n}^{*}\) and \(\gcd(r,n)=1\), ciphertext is \(c=g^{m}r^{m}\text{mod}n^{2}\).
3. Decryption algorithm: Input the private key \(\lambda\) and ciphertext c, output \(m=L(c^{3}\text{mod}n^{2})\mu\) mod \(n\). The Paillier cryptosystem exhibits the following additive homomorphic properties: The product of two ciphertexts encrypted with Paillier, when decrypted, yields the sum of the corresponding plaintexts. Given two ciphertexts \(c_{1},c_{2}\in Z_{n^{2}}\), where \(c_{1}=Enc_{pk}(m_{1})\), \(c_{2}=Enc_{pk}(m_{2})\), the addition operation between ciphertexts is expressed as: \[\begin{array}{ll}c_{1}\oplus c_{2}&=c_{1}c_{2}\text{ mod }n^{2}\\ &=(g^{m_{1}}\cdot r_{1}^{n}\text{ mod }n^{2})(g^{m_{2}}\cdot r_{2}^{n}\text{ mod }n^{2})\\ &=g^{m_{1}+m_{2}}\cdot(r_{1}r_{2})^{n}\text{ mod }n^{2}\end{array}\] (1) where \(r_{1},r_{2}\in Z_{n^{2}}\), \(r_{1}r_{2}\in Z_{n^{2}}\). The resulting ciphertext \(c_{1}c_{2}\text{mod}n^{2}\) corresponds to the encryption of \(m=m_{1}+m_{2}\) mod \(n\).
## 2 Related Work
### Multi-Sink Partitioning in WSNs  
Multiple sinks (multi-sink) usage appears as a solution for large scale networks [24, 25] since it provides some advantages such as energy efficiency, reliability and alleviation of the uneven energy depletion problem of a single-sink deployment [1]. However, deploying more sink nodes in a WSN brings another question: How to partition a sensor network among multiple sinks? The simplest way is for each sink to propagate a message to the whole network to form partitions. However, global flooding from each sink is redundant and costly. To reduce message redundancy and hence the flooding overhead, some flooding scoping techniques such as TTL (time-to-live) scoping or geographical scoping are used. TTL scoping defines a time-to-live for messages disseminated from sinks. In geographical scoping, a node only reformards a flood message if the message came from the closest sink, where \"closest\" means shortest Euclidean distance. One of the geographical scoping methods proposed in [26] is Voronoi decomposition, where scoping decision is entirely distributed. This method describes Voronoi clusters to bound the propagation of messages from different sinks. Each node only rebroadcasts flood messages coming from closest sink, where \"closest\" depends on the underlying distance metric. With this approach, in the steady-state of a network (_i.e._, only after each sink has originated one message) flooding overhead remains constant independently of the number of sinks [26].  
The main focus of this paper is how to maintain the routing trees together with local coverage areas when we have mobile nodes in the sensor network. Our geocasting and mobility management approaches proposed in this paper can be combined with any of the multi-sink partitioning technique using any tree-formation metric to create routing trees rooted at the sinks. In our implementation we have used shortest path routing metric to create the trees, and Voronoi decomposition based on hop-count metric for multi-sink partitioning. The following give the details of our geocasting protocol based on local coverage areas maintained by global updates and describe the new supporting approach, local updates for handling sensor mobility.