esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Literature Review
It measures the latency by leveraging the k-means clustering method in different ways, calculating the distance between the fog nodes and the gateways with distinct approaches that consider the number of fog nodes and the number of gateways. It proved that there are several ways to execute the placement of fog nodes, and all of them lead to much lower latency. Ref, [27] instead considered the creation of a fog network by the means of an online optimization that is capable of improving the functions of the network. The pilot fog node shared the workload with the nearby fog nodes to offload tasks. The online optimization algorithm observed the neighbouring fog nodes and picked the appropriate ones based on their capabilities to form the network. An offload optimization algorithm is used to disperse tasks. Following those methods, the maximum delay in the network is effectively cut down and the task offloading is enhanced. Another paper explored the relocation of the transcoding node closer to the users [28]. The transcoding node would be aware of the user's environment and would receive the media requested. Since the node knows the media solicitor, it can transcode the segments properly for the user; this leads to a system that is flexible when it comes to contrasting users, more efficient with fewer segment levels and more scalable as it can handle more users.
# Change-Point Detection in Autoregressive Processes via the Cross-Entropy Method
###### Abstract  
It is very often the case that at some moment a time series process abruptly changes its underlying structure and, therefore, it is very important to accurately detect such change-points. In this problem, which is called a change-point (or break-point) detection problem, we need to find a method that divides the original nonstationary time series into a piecewise stationary segments. In this paper, we develop a flexible method to estimate the unknown number and the locations of change-points in autoregressive time series. In order to find the optimal value of a performance function, which is based on the Minimum Description Length principle, we develop a Cross-Entropy algorithm for the combinatorial optimization problem. Our numerical experiments show that the proposed approach is very efficient in detecting multiple change-points when the underlying process has moderate to substantial variations in the mean and the autocorrelation coefficient. We also apply the proposed method to real data of daily AUD/CNY exchange rate series from 2 January 2018 to 24 March 2020.  
Keywords:time series; change-point; Minimum Description Length; Cross-Entropy algorithm +
Footnote †: journal:
## 1 Introduction
### Research Significance  
This study focuses on gathering information from the large amount of data produced in recent years concerning landslides. Scientometric study explores the quantitative aspects of the scientific process, science policy, and scientific communication. Its primary focus, involves measuring the influence of researchers, journals, articles and institutions, as well as comprehending citations associated with them. It also focuses on scientific field mapping and visualization, as well as assessment of indicators for future policy and corporate strategy. It is used as a tool for evaluating the outcomes of research and analyzing large data sets which are often too large to consider storing on a single computer system or to be managed by conventional database structures, measurement bundles, or normal visual programming. Scientometric analysis includes the annual production of scientific publications, the most productive countries, and collaboration between groups; additionally, there are many distinct keyword occurrences, as well as progression of study from subject to theme.  
The three techniques primarily used in landslide assessment are reviewed in this study. Probabilistic landslide risk assessment has the virtue of being suitable for all kinds of landslides, if required, and can provide risk maps and curves. In probabilistic models, for the results to be significant, the data must cover a long enough time period to cover both active and tranquil periods. The predictive power and ability to mimic the physical processes that determine landslide incidence make the physically based model an obvious choice for rain-induced shallow landslide hazards. To assess the danger of rainfall-induced landslides, this approach combines distributed hydrological and stability models. The linked models use a physical slope concept, such as the limitless gradient model, along with critical geomechanically and hydrological factors, to predict the spatially distributed safety margin. Landslide susceptibility study approaches have been developed in the scientific literature as a result of recent improvements in remote sensing and geographic information systems (GISs). Landslide monitoring has made use of optical remotely sensed imaging, SAR interferometry takes advantage of the phase differences between multiple SAR photos of the same scene taken at various times. This study also depicts the intensity and frequency of landslide prone regions and how these areas cope with these environmental risks. The major focus is on rainfall-induced landslides which are common in India, Italy, China and USA. When forming research collaborations, joint ventures, and when sharing new thoughts and technologies, researchers from various geographical regions may benefit from the modeling of many parts of the literature developed through scientometric analysis. In addition, this work also highlights significant research areas and open issues.
## 2 Related Work
### Knowledge Graph Embedding Models  
Knowledge graph embedding methods include RESCAL [13], which allows for deep interaction between the information of entities and relationships. However, RESCAL is prone to overfitting, and its complexity increases significantly as the dimensionality of the relationship matrix grows. DisMult [14] relaxes the constraints on the relationship matrix, but it overly simplifies the RESCAL model, making it capable of solving only symmetric relationships in the knowledge base, and not other types of relationships. Additionally, due to the presence of noise in the data and the difficulty in calculating confidence, DisMult is challenging to apply directly to non-deterministic knowledge graphs. To address the issues of DisMult, ComplEx [12] extends DisMult to the complex space representation. After this extension, ComplEx can handle both symmetric and asymmetric relationships. Due to its advantages, ComplEx is widely used in knowledge graph question answering. For temporal knowledge graphs, which include timestamps, additional processing of temporal information is required. TimePlex [15] represents temporal sequences by decomposing them into multiple components, emphasizing trends, seasonality, and residuals. This approach helps analyze the intrinsic structure of time series and is suitable for various time series analysis tasks. TComplEx [16] builds upon ComplEx by introducing temporal information, using complex numbers to represent time series embeddings, and emphasizing periodicity and phase information. This method is capable of representing both the amplitude and phase of a time series, making it particularly useful for capturing periodic patterns.
# Safety of Nuclear Power Plants with Respect to the Fault Displacement Hazard
## 5 Sensitivity/Vulnerability of the Fundamental Safety Functions to the PGD
### Containment Function  
Since the containment is the ultimate barrier and the reactor, the reactivity control systems and part of the emergency heat removal systems are located in the containment, the integrity and leak-tightness of the containment is the ultima ratio and it is reasonable to start the considerations with the containment response to PGD.  
In both studies [22; 41] the integrity of the concrete containments was not threatened by the differential movements below foundation. Moreover, the fragility analyses [23; 24] demonstrated rather low conditional probability of failure of the BWR reactor containment and foundation for the \(P_{F}<0.01\) for the PGD of dip-slip movement \(D>0.3\) m.  
The containment and the adjacent safety and control buildings are on the common rather rigid base mat. There are seismic gaps between the buildings for avoiding the interactions. The PGD should not cause differential movement between the building on common base mat. The differential movement between the buildings on separate foundation can affect the integrity of pipelines and cables connecting the adjacent buildings, and the underground lifelines should be checked for differential settlements. Generally, these pipelines and cables passing through building interfaces are designed for allowing differential movements due to seismic ground motions.  
Leak tightness is ensured by a steel liner of the inner containment wall that is sized for internal pressures and temperatures due to loss-of-coolant accidents. The liner is a ductile structure. The strains in the liner can be evaluated in accordance with ASME Section III Division 2 Table CC-3720-1. The analysis for uneven settlement performed in [41] demonstrated that the strains in liner are below allowable limits, even if the ultimate deformation of concrete walls of hermetic boundary of the containment is assumed.  
The safety systems inside the containment and safety buildings are composed by vessel and piping type components fixed to the walls and floors. The containment inner concrete and steel structures provide reliable support for these components, but the effects of tilting should be analyzed.
# ERCP: Energy-Efficient and Reliable-Aware Clustering Protocol for Wireless Sensor Networks
## 2 Related Work
Although prior research on similar algorithms in [3; 9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24] has successfully reduced the energy consumed to optimize the network's lifetime, they have not accounted for lossy links caused by fading and interference. Ignoring such a problem might increase data loss, retransmission delay, and energy waste. Motivated by the above discussion, we propose an energy-efficient and reliability-aware clustering protocol (ERCP). The ERCP involves two levels, the cluster head selection and the inter-cluster routing protocol. The cluster head selection considers the average intra-cluster distance, cluster load, residual energy, and reliability of intra-cluster data transfer. To achieve reliable intra-cluster data transfer, a new link quality metric function is proposed to express the quality of wireless connections between each candidate cluster head and its member nodes. In the cluster hierarchy, aggregating the data by each cluster head from its members causes imbalanced energy loss. To address such a problem, in addition to energy balance, it is essential to balance the load among cluster heads. The cluster load is used as another factor in the cluster head selection to balance load among cluster heads. We assess the average intra-cluster distance and nodes' energy to improve end-to-end latency and energy efficiency, as with many previous clustering approaches. The second level of this work is the inter-cluster routing protocol, which will be invoked after the clustering phase for data transfer to the sink node. It considers three main parameters: Link quality, nodes' energy, and distance to sink node to optimize network energy efficiency and throughput. Link quality is considered to prevent data forwarding via unstable paths. This is a novel function that combines sensor node residual energy and traffic load and balances the energy usage. The distance factor is considered to minimize the consumed energy and delivery delay. Therefore, the suggested routing approach uses more realistic parameters than prior systems. Table 1 provides an overall comparison of the above-mentioned algorithms.
# Carbon Nanotubes-Based Hydrogels for Bacterial Eradiation and Wound-Healing Applications
## 4 CNT-Based Antibacterial Applications
### Antibacterial Activity of Pristine CNT
The Gram-positive bacteria have more resistance to CNTs because of the thicker peptidoglycan cell wall than Gram-negative bacteria [111]. However, more studies are needed better to understand the different tolerances of diverse bacteria to CNTs.
# Deep Multi-View Clustering Based on Reconstructed Self-Expressive Matrix
## 3 Our Method
### Training Procedure  
The experimental procedure consists of three steps. The first step is to train the autoencoder to extract meaningful latent representations. The second step is to freeze the  
Figure 2: The framework of reconstruction module. The input of this module is the latent representations \(Z^{(v)}\) extracted from each view of the dataset. The specific MLP layers and shared MLP layers are applied to aggregate the commonness and inconsistency of each features and reconstruct self-expressive matrix for every view of the data points. The final clustering result is yielded by the average of \(C^{(v)}\) through spectral clustering algorithm.  
parameters of autoencoder and train the parameters of reconstruction module. The last step is to integrate the self-expressive matrix of each view and obtain the final clustering results. Due to the dependence of the loss function of the reconstruction module on the entire dataset, the memory utilization increases linearly with the number of data points. This hinders its capability to handle large-scale datasets. We use the two-pass algorithm with a constant memory complexity introduced in [45] to train this module. Once we have finished training, we can acquire the latent features of the full dataset by autoencoder and use the reconstruction module to approximate the self-expressive matrix. The training process is shown in Algorithm 1. We can perform spectral clustering by calculating the average of \(C^{(p)}\) of each branch to obtain the final clustering results.  
```
0: Dataset \(X^{(v)}\in\mathbb{R}^{D\times N}\), model parameters \(\Theta^{(v)}=(\Theta^{(v)}_{e}\Theta^{(v)}_{s}\Theta^{(v)}_{d})\), pre-trained epochs \(n\), number of iterations \(T\), learning rate \(\eta\), hyper-parameter \(\lambda\).
1: Initialization: Initialize the network parameters \(\Theta^{(v)}\);
2:while The algorithm has not converged do
3: Train and update \(\Theta^{(v)}_{e}\) and \(\Theta_{d}(v)\);
4: Learn the latent representations \(Z^{(v)}\) using autoencoder by minimizing Equation (4).
5:endwhile
6:for\(t=1\) to \(T\)do
7: Compute \(Z^{(v)}_{C}\) using the Shared MLP layers;
8: Compute \(Z^{(v)}_{S}\) using the Specific MLP layers;
9: Compute \(C^{(v)}\) by Equation (2);
10: Update \(\Theta^{(v)}_{s}\) using the two-pass algorithm by the loss function of Equations (5) and (6).
11:endfor
12: Perform spectral clustering approach using the affinity matrix \(C=\frac{1}{v}\sum_{i=1}^{v}C^{(v)}\);
13:The final cluster labels \(Y\);
```  
**Algorithm 1** Training process of DCRSM.
###### Abstract  
The effectiveness of feature extraction is a critical aspect of fault diagnosis for petrochemical machinery and equipment. Traditional entropy analysis methods are prone to disruption by noise, parameter sensitivity, and sudden entropy variations. This study establishes a high-dimensional mapping entropy (HDME) method characterized by robust noise resistance, addressing the issues of parameter sensitivity and inadequate noise suppression inherent in traditional feature extraction methodologies. A mapping theory of high-dimensional space based on kernel function pattern recognition is proposed, which reassembles the sample vector after phase space reconstruction of time series. The multi-scale high-dimensional mapping entropy (MHDME) and refined composite multi-scale high-dimensional mapping entropy (RCMHDME) algorithms are further studied based on the idea of refined composite multi-scale. Application to simulated signals shows that the suggested methods reduce parameter sensitivity and enhance entropy smoothness. The development of a methodology to identify faults through MHDME is proposed. This approach integrates signal preprocessing and intelligent preference techniques to achieve pattern recognition of reciprocating compressor bearings in various wear conditions. Moreover, the identification findings demonstrate that the suggested approach can effectively extract the characteristics of the signal and accurately distinguish the effects caused by different faults.  
high-dimensional mapping entropy; sample entropy; feature extraction; reciprocating compressors; fault diagnosis +
Footnote †: journal: Applied Sciences
# Intelligent Diagnosis of Compound Faults of Gearboxes Based on Periodical Group Sparse Model
## 2 Materials and Methods
### Proposed Fault Diagnosis Method
#### 2.2.2 Main Steps in the Proposed Method  
In summary, the main steps of the method proposed in this paper are as follows:  
1. Decoupling of compound fault signals using a periodic group sparse model.
2. Wavelet packet energy feature extraction: The single fault features decoupled in the first part are extracted using wavelet packet energy features to fill in the gaps in the reconstructed signal except for sparse non-zero terms, so as to improve the data quality and thus the characterization ability of the dictionary. At the same time, compared with the traditional signal directly used for segmentation, the dimensionality of the data after wavelet packet processing is reduced, which reduces the computational time and increases the differentiation between different health states.
3. Dictionary training and learning: Set the category labels of different health states, and import the wavelet packet energy features into the dictionary classification model for dictionary training. By discriminative sparse coding in the model, the dictionary and classifier are obtained by solving the model using algorithms such as K-SVD and OMP.
4. Fault classification and identification: The test samples are decomposed on the dictionary to obtain sparse representations of different health state signals, and after calculating the sparse coefficients and classifier products, the index term corresponding to the largest element of them is used for fault identification and classification.
## 5 Open Research Issues and Future Directions
For instance, energy efficiency is an issue that needs to be at every layer of the protocol stack, so instead of a strict layered approach, a cross-layer design is desirable. As an example, incorporation of resource awareness in compression schemes, for example dependency on remaining energy, requires coordination between application layer compression and the physical layer. Very little work has been done in cross layer based optimization [128, 129]. Exploration of this aspect of compression in WSNs is necessary. * _Novel Bio-mimetic Algorithms_: Bio mimetic algorithms which have been recently developed could be better alternatives to the existing algorithms. The Firefly Algorithm (FA) is such a  
Figure 9: A representation of number of papers published addressing optimization problems in WSN using bio-mimetic methods (non-exhaustive). novel algorithm which was developed by [PERSON] in 2007 [132,133]. It is based on the flashing patterns and behavior of fireflies. A discrete version of FA can efficiently solve NP-hard scheduling problems [132], while a detailed analysis has demonstrated the efficiency of FA over a wide range of test problems, including multi objective load dispatch problems [133]. * Cuckoo search (CS) is one of the latest nature-inspired metaheuristic algorithms, developed in 2009 by [PERSON] and [PERSON] [28]. CS is based on the brood parasitism of some cuckoo species. In addition, this algorithm is enhanced by a special type of randomization named \"L'evy flights\" [134], rather than by simple isotropic random walks. Recent studies show that CS is potentially far more efficient than PSO and GAs [135]. Bat-inspired algorithm is another novel bio-mimic optimization algorithm developed by [PERSON] in 2010. This bat algorithm is based on the echolocation behavior of micro bats with varying pulse rates of emission and loudness [29]. So the consideration of these novel approaches in WSNs for optimization purposes can be a future direction. * _Real Implementation_: From the above study it is found that many bio-mimic optimization methods have outperformed conventional methods under certain environments. However, most existing bio-mimic optimization works are simulation-based and only a a few have been evaluated in real WSN environments. Implementation of these methods in real WSN environments or test-beds could be a fruitful future research direction. * _Placement of Implementation_: The implementation of most existing bio-mimic algorithms are in base station or sink (centralized), which need communication between the nodes. This communication can be very frequent and expensive in dynamic WSNs environment. Distributed implementation of these algorithms in lightweight form could be a potential future direction.
## 4 Routing Protocol Design
### BVs Re-Selection Process  
To detect path failures, two approaches can be followed.  
If the data message passing among BVs is carried out by using unicast MAC frame delivery service, MAC-level ACKs are used. Therefore, each BV gets an explicit confirmation of the reception of the data frame it forwards to the next BV or to the final destination, namely the SUV node.  
If instead the data message passing exploits the broadcast service of the IEEE 802.11p interface, and MAC-level ACKs are not used. The confirmation of the correct reception of a data packet can be gained by a BV by overhearing the next BV's transmission. As a matter of example, when BV\({}_{1}\) transmits a packet to BV\({}_{2}\), BV\({}_{1}\) can just overhear the channel for the next transmission slot to find out whether BV\({}_{2}\) has forwarded its transmitted packet. In case BV\({}_{1}\) is the last BV of the chain, i.e., its next hop is the SUV node, overhearing is not effective and a network level ACK mechanism must be in place.  
When one BV finds that it loses connection with the next BV, then it will generate a \(link\_break\) message and transmit it back to the SOV. Once the SOV receives a \(link\_break\) message, it will terminate the transmission along the current BV path and initialize a new path construction process.
## 3 Proposed Approach: Quantum Key Distribution (QKD)
### Analysis and Verification of the Proposed Method  
The theoretical evaluation of the proposed algorithm encompasses an analysis of its computational complexity, encryption/decryption speeds, and resilience against various attack vectors. In particular, the complexity is influenced by three principal components: the BB84 protocol, the AES-128 encryption algorithm, and the LZW compression algorithm.  
BB84 Protocol: The computational complexity of the BB84 protocol is chiefly attributable to its error correction and information reconciliation procedures, which exhibit a polynomial dependency on the key length [18]. The protocol further incorporates low-density parity-check (LDPC) codes and adaptive error-reconciliation algorithms. Notably, the simulation results indicate that these mechanisms can reduce key error rates by 25% under conditions of severe atmospheric interference.  
AES-128: The well-documented complexity of AES-128 depends on both the number of rounds and the block size. In this implementation, the use of 10 rounds with a 128-bit block offers a balanced compromise between security and performance.  
LZW Compression: LZW compression operates with a computational complexity that scales linearly with the size of the input data, making it particularly efficient for UAV applications. When handling datasets exceeding 1 GB, it is observed that the computational complexity of the BB84 protocol increases by approximately 15%. However, due to the linear scaling of LZW, the compression efficiency remains stable. Furthermore, the proposed parallelization of error correction tasks contributes to a reduction in processing delays by 20%.