esa-sceva/satcom-chunk-collection huggingface dataset:
# Probability-Based Concrete Carbonation Prediction Using On-Site Data
## 2 Durability Prediction Approach for Carbonated Concrete Structures
### Analysis of Carbonation Durability  
#### 2.2.1 Carbonation Prediction Using Stochastic Analysis Techniques  
In order to precisely determine a model through a stochastic analysis, sufficient data for random variables are needed. Random variables are obtained by a statistical method with a standard normal distribution. While prior predicted values are calculated using a statistical and deterministic method, the posterior predicted values should be obtained by considering field measurement data reflecting various environmental conditions, uncertainties of material properties, and curing conditions, etc.  
In this study, the Bayesian approach that was used to predict a chloride attack problem by [PERSON] et al. was introduced to precisely predict the carbonation durability [16]. The carbonation prediction approach using Bayesian statistics requires previous on-site data. The probability of uncertainties in initial model variables is \(P^{\prime}(\vartheta)\) and the probability of uncertainties reflecting the field trend from measurement data is expressed as \(P^{\prime\prime}(\vartheta)\). The following relation in Equation (8) between these two probabilities is defined by [PERSON] theorem [24].  
\[P^{\prime\prime}(\vartheta)=c_{1}^{*}P(X|\vartheta)\cdot P^{\prime}(\vartheta) \tag{8}\]  
where \(\vartheta\) is the model variable. In this study, the CO\({}_{2}\) diffusion coefficient (\(D_{CO_{2}}\)) was used as the model variable and \(c_{1}^{*}\) is a constant determined under the former probability condition. \(P(X|\vartheta)\) is the uncertainty of on-site measurement data \(X\) in the presence of model variable \(\vartheta\).  
\[P(X)=P(\vartheta) \tag{9}\]  
Equation (9) is valid for monotonically increasing the problems. It has been used to predict future field measurement of concrete properties such as chloride penetration, compressive strength, creep, shrinkage, and long-term deflection in previous studies [25; 26; 27; 16].
## 4 Modern Cyberattacks Taxonomy
### Common Cyberattacks impacting CPS Taxonomy
#### 4.5.1 Malware  
The COVID-19 pandemic is not the only threat faced by humanity and cyber health infrastructures are overwhelmed. Malware attacks have increased as the pandemic has expanded. Malware is a special type of malicious software to obtain data through extortion. Here, a cyber attacker exploits a flaw to encrypt the victim's data. Malware is spread via email attachments, infected applications, and compromised websites. There is a phenomenal amount of unwanted or malicious software. In fact, Malware is a very generic term that encompasses all unwanted programs that interfere with the productive use of a computer, network, mobile devices, and connected objects. But under this name hides very different sub-families. Among the most used to harm critical cyber infrastructure: ransomwares, backdoors, stealer, rootkits, and droppers. Recent malwares are becoming autonomic (self-moving and rapid-propagation) with advanced features. Cryptomalware typically prevents an organization from accessing elements of its critical systems and uses different methods to extort money. The most well-known ransomware are Cryptolocker, Cryptowall, TeslaCrypt, and Wannacry.  
WannaCry [81; 82] and Samsam [83] are representative examples for ransomwares. If a hospital cannot access its health information system, patient care could be delayed or compromised. Here, establishing backup plans, healthy recovery points, and disaster recovery strategies are essential to deal with ransomware attacks. Ransomware infects an ICS workstation, and medical servers. It spreads via autorun files on network shares, USB drives, and known network vulnerabilities. The ransomware spreads for several days before triggering the encryption process. Multiple machines on both IT and ICS networks are thus infected. Authors of autonomous ransomware can be very sophisticated cyber wise, producing malware that can spread quickly and automatically through a network while evading common antivirus systems and other security measures. The malware exploits known vulnerabilities that have not yet been patched on the CPS network, encrypts the engineering workstation, and spreads to most Windows hosts in the CPS. Most Windows hosts in the industrial network are thus encrypted by the attack, shutting down the control system. The impaired control system is unable to bring about an orderly shutdown. Within a few minutes, the plant operator triggers an emergency safety shutdown.
# A Review of Fault Diagnosing Methods in Power Transmission Systems
## 4 Fault-Types Classification (FC)
### Artificial Neural Network (ANN) Based FC
#### 4.1.3 Probabilistic Neural Network (PNN)  
Probabilistic neural network (PNN) structure proposed by [PERSON] in 1989, another type of FNN [68]. It has four layers namely; input layer, pattern/hidden layer, summation layer and output layer as shown in Figure 4 [69]. [PERSON] et al. used PNN for fault classification and found that PNN classification is 10% higher than FNN [70]. DWT is used to extract features for PNN in [71] with nine pattern nodes. In [72], the authors compared PNN, FNN, and RBFN, execution of PNN requires less training time and provided better classification results.  
Figure 3: Radial basis function network structure.  
Figure 2: A two-layer feedforward neural network.
###### Abstract
However, what differs is how the mutual information-maximizing operations for the LDPC decoders are designed and implemented. Therefore, as a second contribution, we discuss what the approaches from the literature have in common, compare them and, based on that, draw some novel conclusions on information bottleneck signal processing in general. Our conclusions break down information bottleneck signal processing to the learning of parameters of trainable functions. The parameters are tuned to maximize the relevant information under compression. This article is structured as follows. The next section first provides an introduction to the information bottleneck method and its application in coarsely quantized information bottleneck signal processing units. A quite general comparison of the information bottleneck method and a communication system is used to explain the applicability of the information bottleneck method for the design of quantized communication receivers. Afterwards, we substantiate the presented ideas by developing and analyzing a particular information bottleneck receiver structure for an LDPC-encoded transmission over a fading channel. This receiver implements all signal processing operations designed with the information bottleneck method using lookup tables. Section 3 then starts by recalling and comparing other approaches to information bottleneck signal processing for LDPC decoders from the literature that do not use lookup tables. From this analysis, we draw the conclusion that a unified view on lookup table-based and other approaches is considering information bottleneck signal processing as learning the optimum parameters of trainable functions that maximize the relevant information. Finally, Section 4 summarizes and concludes this article.
# Ontology-Based Production Simulation with OntologySim
###### Abstract  
Imagine the possibility to save a simulation at any time, modify or analyze it, and restart again with exactly the same state. The conceptualization and its concrete manifestation in the implementation OntologySim is demonstrated in this paper. The presented approach of a fully ontology-based simulation can solve current challenges in modeling and simulation in production science. Due to the individualization and customization of products and the resulting increase in complexity of production, a need for flexibly adaptable simulations arises. This need is exemplified in the trend towards Digital Twins and Digital Shadows. Their application to production systems, against the background of an ever increasing speed of change in such systems, is arduous. Moreover, missing understandability and human interpretability of current approaches hinders successful, goal oriented applications. The OntologySim can help solving this challenge by providing the ability to generate truly cyber physical systems, both interlocked with reality and providing a simulation framework. In a nutshell, this paper presents a discrete-event-based open-source simulation using multi-agency and ontology.  
ontology; production simulation; multi-agent; digital twin +
Footnote †: journal:  
system, as shown in Figure 1. The underlying concept of this storable and expandable ontology-based simulation is discussed in more detail in Section 4.1.  
The paper is organized as follows. In Section 2, Foundation, the terms ontology and simulation are introduced. Section 3, Related Work, categorizes existing simulations and ontology-based simulations in the realm of production and beyond. In Section 4, Proposed Ontology-based Simulation, the unique features of an ontology-based simulation approach are presented and extended in Sections 5-7, introducing the manifestation OntologySim and the individual components and functionalities in more detail. This paper finishes with a discussion in Section 8 and a Summary and Outlook in Section 9.
## 2 Analysis Procedure
### Stage 2--Construction of Equivalent Monopile Model for OC4 Jacket Support Structure  
For OWTG support structure analysis, the software Bladed (DNV GL, Oslo, Norway) can be employed to implement the analysis. However, this commercial program was not adopted by every research teams due to its cost and the limitation of source code modifications. In this work, a simplified and low cost analysis procedure for OWTG jacket substructure has been proposed. The analysis flowchart for OC4 jacket substructure model is displayed in Figure 2. The first step is to build the CAD (Computer-Aided Design) profile of the OC4 jacket support structure and then simplify it to be a monopile model with the aid of freeware BModes 3.0 [23]. The essential information for equivalent monopile model construction, such as the density, cross-sectional area, young's module and area moment of inertia, are evaluated according to the CAD profile of jacket substructure. Both of the OWTG model and the equivalent monopile model will be employed to investigate the operation loadings. The full jacket substructure model is finally analyzed under these operation loadings with the aid of Software SAP 2000 [24].  
Stage 3--Investigations of OWTG Dynamics under OC4 Load Cases, IEC Design Load Cases and Partial Taiwan Environmental Conditions  
This stage is refers to Step 2 and Step 3 in Figure 2. Step 2 is to investigate the loadings induced by OWTG operation, e.g., the resulted force and moments at tower top, under OC4 load case (Phase I). Step 3 is to inspect the strength of members and joints of OC4 jacket support structure model. The outputs of Step 2 are the inputs of software SAP2000 in Step 3. The impact of wave, current and marine growth is also taken into consideration in the stress analysis. Actual data of wave and current are obtained from the Taiwan Central Weather Bureau (Taipei, Taiwan). The structural strength check of OC4 jacket substructure is accomplished under extreme DLCs of IEC 61400-3.
# Soft Periodic Convolutional Recurrent Network for Spatiotemporal Climate Forecast
## 1 Introduction
We propose Soft Periodic-CRN (SP-CRN) with three types of PRD that improve the prediction accuracy by considering phase shift and non-stationarity of periodicity: Nearby-Time, Periodic-Depth, and Periodic-Depth-Differencing representations, as shown in Figure 1. The experiments compared the model performances to predict one-month ahead for a geopotential height at pressure 300 hPa (ZH300) and sea surface temperature (SST) in an ERA-Interim ( [[https://apps.ecmwf.int/datasets/data/interim-full-moda/levtype=pl/](https://apps.ecmwf.int/datasets/data/interim-full-moda/levtype=pl/)]([https://apps.ecmwf.int/datasets/data/interim-full-moda/levtype=pl/](https://apps.ecmwf.int/datasets/data/interim-full-moda/levtype=pl/)), accessed on 10 May 2021) dataset. Figure 1: The conceptual diagram of the proposed PRD in case of monthly representation. Each cell indicates a saved representation for the future reference. Type PRD-1 refers adjacent months of the last year, and types PRD-2 and 3 refer the same month of prior consecutive years.
## 4 Algorithm Design
### Hybrid Ant Colony Optimization Algorithm
#### 4.2.2 Solution Construction
However, this approach does not account for the capacity constraints of CI, potentially leading to resource conflicts where multiple electric logistics vehicles compete for the same CI simultaneously, resulting in infeasible solutions. Therefore, the dynamic rescheduling mechanism is applied to the initial solution obtained in the first stage to resolve conflicting charging tasks, defined as a charging delay (CD). Vehicle routes satisfying the CD condition are retained, while those that cannot are removed, with all associated customer points reclassified as unserved and transferred to the second stage of construction. Assume that the first-stage initial solution involves a single CI, with vehicles requiring charging listed in arrival order as \(V=\{v_{1},v_{2},\ldots,v_{n}\}\), the set of vehicle routes as \(R\), and the set of routes unable to defer charging as \(D\). The specific steps are as follows:  
Step 1. Charging postponement. Based on the dynamic rescheduling mechanism, the charging scheduling sequence \(\sigma\) is constructed step by step from \(V\), with the aim of maximizing the elimination of charging conflicts. This step may generate vehicle \(v_{k}\), whose charging task cannot be postponed, and its vehicle path is \(r_{k}\). Let \(\sigma=\sigma\backslash\{v_{k}\}\), \(R=R\backslash\{r_{k}\}\), and \(D=D\cup\{r_{k}\}\). Step 2. Customer degradation. Identify all customer points included in the vehicle paths within \(D\), degrade them all to unserved customer points, and then store them in set \(UC\). Step 3. Secondary construction. The second stage employs the same initial solution construction approach as the first stage, with the difference that only proper delivery vehicles are arranged for all customer points in \(UC\) obtained from Step 2. Moreover, the node lists are reduced, and \(N_{UC2}\), \(N_{CI}\), and \(N_{BSI}\) related to CI are no longer part of \(allow_{k}\) so as to avoid charging conflicts with the feasible solutions after charging postponement. When \(UC=\emptyset\), the secondary construction is completed, and the new path constructed using the supplemental construction is denoted as \(r_{add}\). Step 4. Path merging. Merge the remaining feasible paths in the initial solution of the first stage with the complementary paths generated in the second stage, i.e., \(R\cup\{r_{add}\}\). The proposed two-stage construction method optimizes the utilization of limited charging resources. By incorporating BSI, it enhances energy replenishment efficiency, alleviates capacity pressure on CI, and ensures the feasibility of the initial solution.
## 4 Experimental Results and Discussion
### Experimental Study of the Output Performance of the Energy Harvester
#### 4.2.2 Output Performance of FEH
The curves of the experimental results are not smooth, which is caused by environmental interference such as resonance generated by the wind turbine and the ground during the experiment.
# A Distributed Energy-Throughput Efficient Cross-Layer Framework Using Hybrid Optimization Algorithm
## 3 Energy-Throughput Efficient Cross-Layer Solution Using MPNMRA for WUSNs  
The application of WUSNs has gained significant attention in various domains, including soil and electricity grid monitoring, landslide and earthquake detection, oil extraction, mine disaster prevention, border security, and others [20]. However, conventional electromagnetic (EM) wave-based communication faces substantial challenges in underground environments, primarily due to antenna size, channel conditions, and limited communication ranges [2].  
To address these challenges, the MI technique has emerged as an alternative wireless communication solution [2]. Extensive research has been conducted on MI communication for underground wireless sensor networks [14], focusing on the physical layer aspects such as signal analysis and antenna design. However, there is currently a lack of protocols specifically designed for MI-based WUSNs.  
In this article, a distributed energy-throughput efficient cross-layer network utilizing a hybrid marine predator naked mole rat algorithm (DECMN) has been proposed. The objective of this solution is to optimize MI communication within the network through a distributed approach.  
The DECMN protocol framework aims to meet the quality requirements, such as transmission reliability and packet delay, for numerous applications in WUSNs while achieving optimal computational complexity as well as energy efficiency. By optimizing these parameters, the solution increases the overall throughput of the network, making it more practical and efficient for real-time applications.  
By leveraging the MPNMRA algorithm, which enhances the exploration and exploitation characteristics of the basic NMRA algorithm, the DECMN protocol framework offers improved performance in terms of finding optimal solutions. This enables efficient and reliable MI communication within the WUSNs, overcoming the limitations of conventional EM wave-based approaches.  
Figure 1: Flowchart of MPNMRA algorithm.  
Overall, the DECMN protocol framework provides a comprehensive solution for optimizing MI-based communication in WUSNs, considering the specific challenges and requirements of underground environments. It combines the advantages of the MI technique with the enhanced capabilities of the MPNMRA algorithm to achieve energy and throughput-efficient cross-layer communication, contributing to the practicality and effectiveness of WUSNs in various applications.
## 3 Multivariable Polynomial Signature Schemes
### UOV Signature Schemes  
We first describe the UOV scheme [3]. Let \(\mathbb{K}\) be a finite field and \(o,v\in\mathbb{N}\). Let us set \(n=v+o\). An oil-vinegar polynomial is defined as  
\[f(x_{1},\ldots,x_{n})=\sum_{i=1}^{v}\sum_{j=i}^{n}\alpha_{i,j}x_{i}x_{j}+\sum _{i=1}^{n}b_{i}x_{i}+\gamma,\]  
where \(\alpha_{i,j}\), \(\beta_{i}\), \(\gamma\in\mathbb{K}\). Additionally, \(x_{1},\ldots,x_{v}\) are called vinegar variables, while \(x_{v+1},\ldots,x_{n}\) are called oil variables.  
The UOV signature scheme uses a map \(\mathcal{F}:\mathbb{K}^{n}\rightarrow\mathbb{K}^{o}\) known as the UOV map or the central map. This map consist of oil-vinegar polynomials \(f^{(1)},\ldots,f^{(o)}\), i.e.,  
\[\mathcal{F}(x_{1},x_{2},\ldots,x_{n})=(f^{(1)}(x_{1},x_{2},\ldots,x_{n}), \ldots,f^{(o)}(x_{1},x_{2},\ldots,x_{n}))\]  
where \(f^{(k)}(x_{1},x_{2},\ldots,x_{n})=\sum_{i=1}^{v}\sum_{j=i}^{n}\alpha^{(k)}_{i,j }x_{i}x_{j}+\sum_{i=1}^{n}b^{(k)}_{i}x_{i}+\gamma^{(k)}\) for \(1\leq k\leq o\)  
Note that given \(y\in\mathbb{K}^{o}\) we can efficiently find an \(x\in\mathbb{K}^{n}\) such that \(\mathcal{F}(x)=y\) by first randomly choosing values \(v_{1},v_{2},\ldots,v_{v}\in\mathbb{K}\) for the vinegar variables and then solving the resulting linear system of \(o\) equations and \(o\) variables to find the values of the the oil variables. Additionally, the structure of \(\mathcal{F}\) is hidden by composing it with a random invertible linear map \(\mathcal{T}:\mathbb{K}^{n}\rightarrow\mathbb{K}^{n}\) to get a public map \(\mathcal{P}=\mathcal{F}\circ\mathcal{T}\). To describe the UOV signature scheme, we assume to have access to a cryptographic hash function \(\mathcal{H}:\{0,1\}^{*}\rightarrow\mathbb{K}^{o}\).  
**Key Generation Algorithm.**  
Choose all the coefficients of each oil-vinegar polynomial \(f^{(k)}\), for \(1\leq k\leq o\), at random from \(\mathbb{K}\) to construct the central map \(\mathcal{F}\).  
Choose an invertible matrix \(\mathbb{T}\in\mathbb{K}^{n\times n}\) at random to represent the linear map \(\mathcal{T}\).  
Compute \(\mathcal{P}=\mathcal{F}\circ\mathcal{T}\).  
Output \(\mathcal{T},\mathcal{F},\mathcal{P}\), where \(\mathcal{P}\) is the public key, while \((\mathcal{T},\mathcal{F})\) is the private key.  
**Signature Generation Algorithm.** Given a message \(m\) and the private key \((\mathcal{T},\mathcal{F})\), then  
Compute \(y=\mathcal{H}(m)\).  
Compute a preimage \(x\in\mathbb{K}^{n}\) of \(y\) under the central map \(\mathcal{F}\).  
Compute the signature \(s\in\mathbb{K}^{n}\) by \(s=\mathcal{T}^{-1}(x)\) and return it.  
**Signature Verification Algorithm.** Given a message \(m\), a signature \(s\in\mathbb{K}^{n}\) and the public key \(\mathcal{P}\), then  
If \(\mathcal{H}(m)=\mathcal{P}(s)\), return accept. Otherwise, return reject.  
In a practical setting, \(\mathbb{K}\) is normally set to \(\mathbb{F}_{2}\) for the UOV scheme. The LUOV scheme [1] is basically a variant of the UOV signature scheme, and is described next.
## 3 Results
### Doping of HA Nanoparticles
#### 3.3.2 Zinc-Doped Nano-Hydroxapatite (Zn-Nano-HA)  
Zinc (Zn) is an essential trace element and cofactor for numerous enzymes involved in the synthesis of DNA, RNA, and proteins, playing a pivotal role in cellular repair and the regeneration of musculoskeletal tissues [44]. In bone metabolism, zinc is critical for enhancing bone formation and mineralization by stimulating collagen production and increasing alkaline phosphatase (ALP) activity, a marker of osteoblast differentiation [12]. Additionally, zinc inhibits osteoclast activity, thereby preventing bone resorption, which is vital for maintaining bone homeostasis and supporting regeneration [38].  
When combined with hydroxyapatite (HA), zinc enhances bioactivity by improving osteoblast viability, promoting osteogenic activity, and accelerating bone healing. Zinc-doped HA (Zn-HA) has demonstrated antibacterial properties, making it a dual-function material for bone tissue engineering applications [49]. However, zinc's effects are dose-dependent: concentrations in the range of 1-50 \(\upmu\)M optimally stimulate osteoblast activity, while higher concentrations may inhibit osteogenesis [44].  
The mechanisms underlying zinc's bioactivity include its role as a catalytic cofactor in protein synthesis and as a secondary messenger in signaling pathways that regulate cell division and proliferation. Zinc ions (Zn\({}^{2+}\)) released from Zn-HA surfaces increase intracellular zinc levels, which protect cells from oxidative stress and apoptosis. Furthermore, zinc ions stimulate fibroblast proliferation in the oral system, contributing to enhanced tissue regeneration [45].  
Zn-HA scaffolds containing less than 1% zinc ions have been shown to exhibit significant antibacterial properties, reducing the risk of infection while supporting bone regeneration. Additionally, zinc ions can modulate immune cell responses, further enhancing the biological performance of Zn-HA scaffolds [11]. These properties make Zn-HA an invaluable material in bone tissue engineering, combining osteogenic potential, antibacterial efficacy, and immune modulation to create advanced scaffolds for clinical applications.