esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
### Problem Statement and Industrial Challenge  
Software-defined manufacturing systems (SDMSs) address these limitations by overlaying a software control plane anchored by digital twins (DTs) and centralized control mechanisms, enabling more efficient management of complex manufacturing environments [4]. DTs enable manufacturers to visualize, simulate, and optimize manufacturing workflows, monitor real-time operations, and predict equipment performance using sensor data, AI algorithms, and IoT integration [5]. However, DTs' effective implementation requires seamless data integration from diverse sources, including legacy systems, sensors, and enterprise-wide repositories [6]. This complexity often hinders interoperability and real-time decision-making, limiting the full potential of DT-based manufacturing systems.  
To address these integration bottlenecks at scale, researchers have recently proposed the Federated Digital Twin (FDT) paradigm, which enables multiple interconnected DTs to share operational insights across distributed manufacturing sites [7; 8]. Despite these benefits, ensuring real-time concurrency control across synchronized DTs within SDMSs remains an open problem. In distributed FDT environments, concurrent updates to shared resources without proper coordination can cause data conflicts, synchronization failures, and priority inversions, where critical high-priority tasks are delayed by lower-priority operations, undermining Quality of Service (QoS) and delivery guarantees [9; 10]. Traditional concurrency control mechanisms, designed for tightly coupled centralized systems, are ill-suited for these dynamic, distributed interactions [11]. Likewise, legacy rule-based schedulers lack the real-time adaptability required to prevent conflicts under fluctuating loads [12]. These shortcomings underscore the need for a smarter, real-time concurrency strategy tailored to FDT. We therefore propose a hybrid approach that couples Deep Reinforcement Learning (DRL) with the Priority Ceiling Protocol (PCP): DRL provides adaptive, data-driven resource allocation, while PCP enforces dynamically updated ceilings to block priority inversion. We call the hybrid scheduler Deep Reinforcement Learning-based Concurrency Control (DRLCC); the term \"concurrency control\" subsumes the Priority Ceiling Protocol layer, while \"DRL-based\" highlights the adaptive PPO agent.  
Figure 1: Evolution from Industry 1.0 to Industry 5.0.
# Phenomenologies in Hypersphere Soliton and String Photon Models
## 1 Introduction  
In this work, we will consider the extended objects instead of the point particles. It is well known that, as the extended objects, we have the solitons [1; 2; 3; 4; 5; 6; 7; 8; 9] and strings [10; 11; 12; 13]. In particular, in this review we will mainly use the hypersphere soliton model (HSM) [4; 6; 9] and stringy photon model (SPM) [13]. To be more specific, in the soliton models, we have the standard Skyrmion, which describes baryon static properties in \(R^{3}\) manifold [1; 3; 5; 8]. This model was proposed by [PERSON] in 1961 [1]. In this paper we will consider the paper by [PERSON], Nappi and Witten (ANW) [3; 5], to compare with the HSM. Next, we will investigate the HSM, which is formulated on the hypersphere \(S^{3}\) instead of \(R^{3}\)[4; 6; 9]. Exploiting the HSM, we will evaluate the baryon physical quantities, most of which are in good agreement with the corresponding experimental data.  
In 1962, the electron was proposed as a charged conducting surface by [PERSON] [14]. According to his proposal, the electron shape and size should pulsate. Here, the surface tension of the electron was supposed to prevent the electron from flying apart under the repulsive forces of the charge. Motivated by his idea, we will investigate pulsating baryons in the first-class formalism in the HSM [4; 6], to evaluate the intrinsic frequencies of the baryons, baryon masses with the Weyl ordering correction (WOC) and axial coupling constant [9].  
On the other hand, as regards string theories, we have the critical higher-dimensional string theory [10; 11; 12], and the recently proposed SPM defined in four-dimensional spacetime, which predicts the photon radius, and the photon intrinsic frequency comparable tothe corresponding baryon intrinsic frequencies [13]. In the SPM, we have exploited the open string, which performs both rotational and vibrational motions [13]. Note that the rotational degrees of freedom of the photon have been investigated in the early universe [15; 16].  
In this review, we will exploit the HSM in the first-class Dirac Hamiltonian formalism to evaluate the physical quantities such as the baryon masses, magnetic moments, axial coupling constant, charge radii and baryon intrinsic frequencies. Next, in the SPM, we will predict the photon intrinsic frequency, which is shown to be comparable to the baryon intrinsic ones. To do this, we will exploit the Nambu-Goto string theory [17; 18]. In the SPM, we will next introduce an open string action associated with the photon [19]. Making use of the rotational and vibrational energies of the string, we will evaluate explicitly the photon intrinsic frequency with which, assuming that the photon size is given by the string radius in the SPM, we will predict the photon size.  
In Section 2, we will predict the baryon properties in the HSM. In Section 3, we will evaluate the intrinsic frequencies of the baryons in the HSM. In Section 4, we will exploit the SPM to predict the photon intrinsic frequency and photon size. Section 5 includes conclusions.
## 1 Introduction
### Literature Review
A new decision-making approach involves the setup of a specific experimental procedure that can propose an optimized mixture composition with the lowest fuel price combined with the appropriate fuel density. Thus, the experiment process in the laboratory is more effective, concentrating the research interest around better results. Sophisticated methods drawing from natural phenomena, bio-inspired computational intelligence, algorithms in machine learning, and evolutionary computation strategies yield superior outcomes close to the optimal for intricate optimization challenges. Consequently, the practical applications of operational research rely on their utilization and advancement, i.e., on resource leveling and fuzzy clustering [25; 26]. Machine Learning (ML) and Artificial Intelligence (AI) approach [27; 28], and especially more than 100 studies were presented in recent years [29] focusing on the application of ML and AI on different aspects of renewable and sustainable energy [30; 31], i.e., bioenergy [32] and low-carbon energy advancement [33], power dispatch systems [34], chemical process systems [35], and particularly biodiesel production [36] and conversion [37], as well as microalgal biofuel development [2; 38], amplifying the design, handling, control, optimization, and monitoring. Specifically, relatively powerful methodologies employed include the following ML and AI algorithms evolved with deep learning:  
* Linear Regression
* Principal component analysis (PCA),
* Genetic Algorithms (GA),
* K-Nearest Neighbors (KNN)
* Random Forest Regression (RF),
* Artificial Neural Networks (ANNs) or simulated neural networks (SNNs),Support Vector Machines (SVMs). * Fuzzy Multi-Criteria methodologies  
The algorithms discussed above can demonstrate superior predictive capabilities, boasting the highest levels of accuracy among methods utilized in Biodiesel production. Their effectiveness stems from the brain's inherent ability to learn and improve autonomously, tackling complex challenges posed by the survey. Consequently, these algorithms prove immensely valuable for modeling trans-esterification processes, studying physicochemical properties, real-time monitoring of biodiesel systems, analyzing emission composition, estimating temperatures, and assessing engine performance during the combustion phase. Some algorithms provide fatty methyl acid ester as an output, and they take different types of oil and catalyst as inputs, methanol-to-oil ratios, catalyst concentrations, reaction rates, domains, and frequencies. However, the aforementioned approaches concentrate on biodiesel mixture properties optimization via the prediction of the conversion into biodiesel under various conditions. Moreover, they do not suggest an optimal mixture on the basis of raw material availability, considering both cost and density, as achieved by the Genetic Algorithm for the optimal Fuel Mixture Problem presented hereunder.
# A Node Placement Algorithm Utilizing Mobile Nodes in WSN and IoT Networks +
## 6 Evaluation
### Experimental Results
#### 6.3.3 Evaluation of eNPA with Different Energy Models
In this scenario, the mobile node in the network returns to its initial position due to energy lack, and a new mobile node takes the second congestion. It is shown that until the mobile node leaves, all models have the same reaction; when the mobile node leaves and until the new one is placed in its position, the energy does not change much. The changes in the consumption are due to the energy from the source node that is sending their packets and their relay nodes. When the new mobile node is inserted into the network, the energy consumption increases faster than before. Figure 12: Evaluation of the different Scenarios. (**a**) Energy Consumption of Scenario 1, (**b**) Energy Consumption of Scenario 2, (**c**) Energy Consumption of Scenario 3A, (**d**) Energy Consumption of Scenario 3B. In Figure 12d, we present the results of each energy model for scenario 3B. In this scenario, the mobile node returns back to its initial position due to energy lack, but it will be replaced by another mobile node because it is needed in the network. When the second congestion occurs, a new mobile node is inserted into the network. As a result, this scenario has the largest number of mobile nodes used and the most energy consumed. All models start to differ when the replacement is made, and the source node sends more packets in order to create the second congestion. Before the end, a new mobile node enters the network and the consumption increases. The highest number is returned by energy models 2 and 3, which use the time, whereas energy model 1 that uses the distance, consumes the least energy because the distance does not change as often as the time. In general, we can observe that energy model 1 has the least energy consumption in the network because the distance variable is not as frequently changed as the time variable. The two models, 2 and 3, that use time, which changes at all times, have more consumption. The speed variable, which is a constant variable, does not really affect the result. Discussion  
The evaluation of our algorithm based on different energy models was to observe the impact of the model on the total energy. The results show that the algorithm has a greater impact on the energy consumption of the node than the energy model. All energy models used in the evaluation provided similar results with a small difference depending on specific details. This is expected since the energy models are based on the same general one that relates to speed, time, and distance. As a result, we can observe that the energy consumption of the nodes depends mainly on the algorithm and the steps they follow, rather than on the specific energy model used.
## 2 Materials and Methods
### Testing Methodology
#### 2.2.6 Statistical Analysis  
The obtained results of the heat flux and electric power tests were subjected to statistical analysis in order to determine the statistical significance of the obtained differences. Before starting the analysis, the normality of the distribution of variables was checked using the Shapiro-Wilk test. Due to the non-fulfillment of the normality criterion, the non-parametric Kruskal-Wallis test was used for the statistical analysis, and in the case of statistically significant differences, the multiple comparison test of mean ranks was applied for their detailed analysis. The test significance level was set at 0.05. The statistical analysis was performed with the use of the STATISTICA 13.1 PL software.
## 3 Results
### EOs Rich in Alpha-Phellandrene and Their Main Biological Activities
#### 3.4.2 Antitumoral Effects  
Another important investigation area which involved EOs rich in alpha-phellandrene referred to their antimuoral activities. [PERSON] et al. [50] studied this property from the EOs of two species that have high concentrations of this molecule, _S. molle_ and _S. tetebinthifolius_ (46.5% and 34.4%, respectively). The IC of the EO to stop MCF-7 human breast cancer cell growth was tested. Using _S. tetebinthifolius_ EO, the IC\({}_{50}\) was 47.0 \(\upmu\)g/mL, whilst it was 54.0 \(\upmu\)g/mL when using _S. molle_. [PERSON] et al. [87] focused on the effects of _Monodora myristica_ EO on the same line of tumor cells (MCF-7), finding an IC\({}_{50}\) of 0.265 \(\upmu\)L/mL when alpha-phellandrene was at a concentration of 67.1%. Likewise, [PERSON] et al. [88] found that this molecule, at a concentration of 30.0 \(\upmu\)M, induced necrosis of human -lived tumor (J5) cells, increasing ATP depletion via enhanced nitric oxide and reactive oxygen species production and lactate dehydrogenases leakage. In another study [89], the same group suggested that this molecule could induce J5 cell autophagy by regulating mTOR and LC-3 II expression, p53 signaling and NF-xB in J5 cells. [PERSON] et al. [90] studied the effect of the EO from the leaves of _Conobea scoparioides_ (alpha-phellandrene 13.1%) against three different lines of human tumor cells, and, in particular, line MCF-7 for human breast adenocarcinoma, line HepG2 for human hepatocellular carcinoma, and HCT116 for human colon carcinoma, finding that the EO displayed in vitro cytotoxicity against all of them.  
A study from Jordan [91] found that the EO from _S. molle_, which ranged in concentration of alpha-phellandrene between 40.8% and 55.9%, haf a moderate antiproliferative activity on the colorectal and breast cancer cell lines (HCT116, Caco-2 and MCF7, T47D, respectively) with an IC\({}_{50}\) ranging between 21.0-65.0 \(\upmu\)g/mL.
## 2 Materials and Methods  
### The Seismic Data Used  
The seismic data used come from the Turkish Disaster and Emergency Management Authority (AFAD) and are publicly available at the site [[https://deprem.afad.gov.tr/event-catalog](https://deprem.afad.gov.tr/event-catalog)]([https://deprem.afad.gov.tr/event-catalog](https://deprem.afad.gov.tr/event-catalog)) (last accessed on 7 May 2023). We considered all EQs within the region N\({}^{46}_{33}\)E\({}^{48}_{20}\) (see the yellow rectangle in Figure 1).  
The frequency-magnitude distribution (FMD) for the EQs during the period 1 January 2002 to 31 December 2022 is shown in Figure 2a, the reported magnitude in 93% of these cases is the local magnitude ML; otherwise, moment magnitude \(M_{w}\) was reported; see [[https://deprem.afad.gov.tr/event-catalog](https://deprem.afad.gov.tr/event-catalog)]([https://deprem.afad.gov.tr/event-catalog](https://deprem.afad.gov.tr/event-catalog)) (last accessed on 7 May 2023). For AFAD, it is reported that ML and \(M_{w}\) scales are almost equivalent for a wide range of magnitude values (see [78] and references therein); thus, this catalog could be considered homogeneous with respect to magnitude (cf. a unified EQ catalog is necessary for this kind of analysis, see, e.g., [79]). There, beyond the cumulative FMD depicted by open squares, the non-cumulative FMD [80] is also shown with open triangles. According to the Gutenberg-Richter (GR) law [81] for EQs, the number of EQs \(N(M>m)\) of magnitude (\(M\)) greater than \(m\) follows an exponential decay  
\[N(M>m)=10^{a-bm}, \tag{3}\]  
where \(a\) reflects the total rate of seismicity in the region and the \(b\)-value (which is usually close to unity, see, e.g., [23]) is related to the level of seismicity in the region (c.f. for a possible correlation between these two parameters see [82]). As shown in Figure 2a (obtained by the standard code ZMAP [83] using maximum likelihood estimation, see, e.g., [84]), the GR law of Equation (3) is valid for \(M\geq M_{c}\), where the magnitude of completeness \(M_{c}\) equals 3.5. We also examined that setting \(M_{c}\) to the latter value, \(m=3.5\), secures temporal completeness since 1 January 2002; see Section 3 below. For the readers' better information, we note that new methods for the estimation of the magnitude distribution parametershave been recently proposed in the literature [85; 86]. Moreover, further details on the seismicity of Turkey and its monitoring can be found in [71] and references therein. Since it will be needed later for the analyses presented in Section 3, we also investigated in Figure 2b the completeness of the catalog obtained from AFAD during the period from 1 January 2020 to 1 January 2023 within a 3\({}^{\circ}\times\) 3\({}^{\circ}\) area centered at the actual epicenter of the Kahramanmaras-Gaziantep \(M_{w}\)7.8 Pazarcik EQ.
# An Efficient SS-MAC Protocol for IEEE 802.15.4-Based WSNs of Cluster Tree Topology
## 3 The Proposed SS-MAC Protocol  
This section details the design and mechanism of each network phase of the proposed SS-MAC.
## 7 Summary, Conclusions, and Discussion
### Directions for Future Work  
Future work could address the following items:  
* Studying the effects of positioning considering the rotation along the vertical axis and tilting on the horizontal axes of the 2D sensor array.
* Modeling of the 2D sensor array as, for example, an antenna array, considering the effects of constructive and destructive waves for a given antenna spacing, the AoA of the radio-waves, etc.  
* A performance evaluation of a 2D sensor array using datasets obtained from field measurements.
* Exploration of alternative pattern-matching methods or an enhancement to the proposed methods.
* The use of other tile arrangements.
* The use of overlapping fixed tiles.
* The use of any other tile format or shape. For example, an interesting tile format to consider would be hexagonal-shaped tiles.
* The use of spatial side information collected using a kind of 2D sensor array with sparse sensors--for example, distributed along the machinery or object(s) performing the positioning.
* Alternative mappings from the tile-classes to the positions using pattern-matching method 4-MultFingIn-ClassOut.  
Conceptualization, S.L.; methodology, S.L.; software, S.L.; validation, S.L. and S.H.; formal analysis, S.L.; investigation, S.L.; resources, S.H., S.R., T.C., and P.K.; initial experimental dataset generation, S.H., L.T., and S.L.; hardware setup and support, S.R.; hybrid positioning system consulting, P.K.; writing, S.L.; review, S.L, S.H., and P.K.; visualization, S.L.; supervision, S.H. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The datasets used in this study were synthetically generated using the system simulator described in Section 5. A description of how to generate the datasets to reproduce the results is contained within the article in Section 5.2 (Generation of Datasets of Fingerprints). The datasets can be generated by implementing the first seven steps of the system simulator explained in Section 5.6.1.  
All of the authors are employed by VTT Technical Research Centre of Finland Ltd.
# A Large Bridge Traffic Operation Status Impact Assessment Model Based on AHP-Delphi-SVD Method
## 2 Requirements for Detection Indicators of Bridge Facility Operation Status
### Determination of Existing Information and Analysis of Factors Affecting Traffic Operation Status  
The existing information about large bridges is defined as the data for the current evaluation object held in various information systems included in the road network. From the perspective of information access, analysis and processing, and system evaluation, the existing information about large bridges can be divided into two categories: static information and dynamic information:  
1. Static information refers to the data of regarding the attributes of routes and bridges included in the road network, which are generally not changed after the completion of the bridge.
2. Dynamic information refers to the real-time monitoring results of the dynamic monitoring system for large bridges in the highway network, the detection results of the maintenance, management, and monitoring of systems, and various results of daily inspections. This part of the data usually has a certain update frequency according to management needs.  
The factors affecting bridge traffic are mainly divided into two categories: the impact of each component of the main structure and each component of the bridge deck system on traffic.  
Among them, the impact of each component of the main structure on traffic includes:  
1. Superstructure traffic-influencing factors refer to the detection results of the bridge located above the support, including the bridge span structure and bridge deck structure.
2. Substructure traffic-influencing factors refer to the detection results of the supporting structure of the bridge located below the support.  
The impacts of various components of the bridge deck system on traffic include:  
1. Traffic-influencing factors of bridge deck paving refer to the detection results for bridge deck pavement.
2. Traffic-influencing factors of expansion joint devices refer to the detection results for expansion joint devices in bridge deck systems.
3. Traffic-influencing factors of drainage system refer to the results of an inspection of the drainage system in the bridge deck system.  
Figure 1: Hierarchy of bridge traffic impact assessment.  
(4) Traffic-influencing factors of lighting facilities refer to the results of an inspection of the lighting facilities on bridge decks.
# A Design of CGK-Based Granular Model Using Hierarchical Structure
## 3 IG Creation and Granular Model Design
### Creating Rational IG
RMSE evaluates performance by subtracting the model's predicted values from the actual predicted values, calculating the mean of the squares, and squaring the obtained value. MAPE evaluates performance by subtracting the model's predicted value from the actual output value and dividing by the model's predicted value. These performance evaluation methods are mainly used when the model's output value is a numerical value. However, in the case of granular models comprise IGs, the model output is not a numerical value but an IG; hence, it is difficult to evaluate the model using general performance evaluation methods. To address this issue, studies are actively being conducted on adopting coverage and specificity as performance evaluation methods for granular models [45; 46; 47; 48; 49]. The higher the PI, the more meaningful the IG, and granular models with excellent performance can be designed. \[\textit{Performance index}=\textit{coverage}(\varepsilon)\cdot\textit{specificity}(\varepsilon) \tag{18}\]  
The PI value obtained from a granular model can be adopted to represent the relationship between coverage and specificity as coordinates, and the changes in model performance, which are related to changes in the PI value, can be observed. Figure 5 illustrates the trade-off relationship between coverage and specificity. If coverage approaches zero, specificity approaches one, and the shape of the IG approaches a point. It can be observed that as the coverage increases, the size of the IG increases, but the specificity decreases.