esa-sceva/satcom-chunk-collection huggingface dataset:
# The Role of Multi-Agent Systems in Realizing Asset Administration Shell Type 3
## 3 AAS Type 3 Approaches Based on Multi-Agent Systems  
As previously discussed, given the advantages provided by MAS, along with their shared characteristics and conceptual alignment with AAS Type 3, particularly regarding autonomy, intelligence, and collaborative capabilities, the current literature has increasingly considered MAS as a suitable approach for enhancing AAS solutions, often referred to as agent-based AAS solutions. Building on this perspective, this section examines the role of MAS in existing agent-based AAS approaches and explores their potential to support the realization of AAS Type 3. Moreover, it is important to highlight that a few other studies are also investigating AAS Type 3, namely [23, 25]. However, due to the current trend in  
Figure 2: Multi-Agent Systems overview. the literature of using MAS to enhance AAS capabilities, this work focuses exclusively on agent-based AAS approaches. To identify existing agent-based AAS approaches in the literature, a bibliographic search was conducted in the Scopus database using the keywords \"Asset Administration Shell\" and \"Multi-Agent Systems\", along with variations of these terms, combined using the AND operator. The search was restricted to publications from 2015 to 2025 (first semester), written in English, and classified as journal articles, conference papers, or book chapters. Additionally, only documents in which the search terms appeared in the title, abstract, or keyword fields were considered. From the initial set of 35 documents retrieved, a screening process was carried out to identify the most relevant studies for in-depth analysis. Several factors may account for the relatively low number of documents: (i) the AAS concept is still recent, having been formally introduced only in 2015; (ii) much of the research over the past decade has focused on consolidating foundational concepts, particularly related to AAS Types 1 and 2; (iii) overall, the current literature on AAS remains limited, with only 503 documents identified through a broader Scopus search using the keyword \"Asset Administration Shell\". Following the screening process, Table 3 summarizes the reviewed research works, offering valuable insights into how MAS have been integrated with AAS to enhance their capabilities. These studies cover a wide range of application domains where agent-based AAS interact to support autonomous, collaborative, and intelligent processes in industrial environments. The identified applications include smart manufacturing, dynamic production replanning, shared production, control, scheduling, human collaboration, diagnosis, and fault detection. This diversity illustrates the versatility of agent-based AAS, showing how they can be effectively adapted to meet the specific demands and operational contexts of various industrial scenarios.
## 2 Theoretical Method and Physical Model
### Solution of Oil Film Reynold Equation
#### 2.1.3 Solving Method
\tag{11}\]  
\[\left(\frac{\partial H}{\partial\theta}\right)=\frac{H_{i+\frac{1}{2},j}-H_{ i-\frac{1}{2},j}}{\Delta\theta} \tag{12}\]  
Substituting Equations (10)-(12) into (9), the Reynolds equation can be obtained as [20]  
\[A_{i,j}(P_{i+1,j})_{1}+B_{i,j}(P_{i-1,j})_{1}+C_{i,j}(P_{i,j+1})_{1}+D_{i,j}(P _{i,j-1})_{1}+E_{i,j}(P_{i,j})_{1}=\left(F_{i,j}\right)_{1} \tag{13}\]  
Among them, the following items are included  
\[\left\{ \begin{matrix}A_{i,j}=\frac{H^{3}_{i+\frac{1}{2},j}}{\Delta\theta^{2}},B_{i,j}=\frac{H^{3}_{i-\frac{1}{2},j}}{\Delta\theta^{2}},C_{i,j}=\left( \frac{2R}{\mathcal{I}}\right)^{2}\frac{H^{3}_{i+\frac{1}{2}}}{\Delta\lambda^ {2}},D_{i,j}=\left(\frac{2R}{\mathcal{I}}\right)^{2}\frac{H^{3}_{i-\frac{1}{2 }}}{\Delta\lambda^{2}}\\ (F_{i,j})=6^{\frac{H_{i+\frac{1}{2}},j}{\mathcal{I}\Delta\theta}-H_{i-\frac{ 1}{2},j}}+\frac{6\varepsilon\cos\theta}{R\mathcal{I}\Psi}\\ E_{i,j}=A_{i,j}+B_{i,j}+C_{i,j}+D_{i,j}\end{matrix} \right. \tag{14}\]  
where \((P_{i,j})_{1}\) and \((P_{i,j})_{2}\) are the oil film pressures of the inner and outer layers, respectively, and \(A_{i,j}\) to \(F_{i,j}\) are the equation coefficient matrices. The final dimensionless expression of the oil film pressures of the inner and outer layers is derived as  
\[(P_{i,j})=\frac{A_{i,j}(P_{i+1,j})+B_{i,j}(P_{i-1,j})+C_{i,j}(P_{i,j+1})+D_{i, j}(P_{i,j-1})-(F_{i,j})}{E_{i,j}} \tag{15}\]  
\(P_{i,j}\) of all nodes is expressed by the above formula, and a set of \((m-1)(n-1)\) inhomogeneous linear equations can be formed. According to the Reynolds boundary condition: the lubricating oil film is continuous between the start point and the end point of the oil film pressure in the circumferential direction of the journal. Then, some boundary conditions are defined, in which \(\lambda=0\), and at \(\theta=0\), \(P=0\). The equations are measured to get the pressure distribution \(P^{1}_{i,j}\); then, it is regarded as the initial value of the next iteration. The iteration termination condition can be obtained as  
\[\frac{\sum\limits_{j=2}^{n}\sum\limits_{i=2}^{m}\left|P^{(k)}_{i,j}-P^{(k-1)}_{ i,j}\right|}{\sum\limits_{j=2}^{n}\sum\limits_{i=2}^{m}\left|P^{(k)}_{i,j} \right|}\leq\delta,\ (\delta\text{ generally go to around }10^{-3}) \tag{16}\]
###### Abstract  
In this work, the performance of the multi-GNSS (Global Navigation Satellite System) Precise Point Positioning (PPP) technique, in static mode, is analyzed. Specifically, GPS (Global Positioning System), GLONASS, and Galileo systems are considered, and quantifying the Galileo contribution is one of the main objectives. The open source software RTKLib is adopted to process the data, with precise satellite orbits and clocks from CNES (Centre National d'Etudes Spatiales) and CLS (Collecte Localisation Satellites) analysis centers for International GNSS Service (IGS). The Iono-free model is used to correct ionospheric errors, the GOT-4.7 model is used to correct tidal effects, and Differential Code Biases (DCB) are taken from the Deutsche Forschungsanstalt fur Luftund Raumfahrt (DLR) center. Two different tropospheric models are tested: Saastamoinen and Estimate ZTD (Zenith Troposhyneric Delay). For the proposed study, a dataset of 31 days from a permanent GNSS station, placed in Palermo (Italy), and a dataset of 10 days from a static geodetic receiver, placed nearby the station, have been collected and processed by the most used open source software in the geomatic community. The considered GNSS configurations are seven: GPS only, GLONASS only, Galileo only, GPS+GLONASS, GPS+Galileo, GLONASS+Galileo, and GPS+GLONASS+Galileo. The results show significant performance improvement of the GNSS combinations with respect to single GNSS cases.  
GNSS; GPS; GLONASS; Galileo; PPP; RTKLib; ZTD 2020; 10.5420; doi:10.3390/app10165420
## 2 Architectural Principles
### M2M Gateway for Interoperability
The third challenge is related to power consumption of the radio access protocols. For example, Bluetooth special interest group (SIG) has specified special low power Bluetooth (Bluetooth low energy (LE, also called as Bluetooth smart). In addition, IETF is working with mapping of 6 LowPan with Bluetooth Smart. There are also challenges arising from heterogeneity and mobility of M2M devices and local M2M asset networks, and coding and integration of M2M application content.
# A Lightweight Chaotic Map-Based Key Agreement Scheme for the Advanced Metering Infrastructure
## Preliminaries
### Chaotic Maps Mathematical Hard Problems  
**Definition 1**.: _CMDLP (Chaotic Map-Based Discrete Logarithm Problem). Discrete logarithm problem on \(T_{n}(x)\): given a large prime number \(P\) and two large integers \(x\) and \(y\), find an integer \(e\), such that_  
\[T_{e}(x)\;mod\;P\;\equiv\;y \tag{5}\]  
**Definition 2**.: _CMDHP (Chaotic Map-Based Diffie-Hellman Problem). Diffie-Hellman problem on extended \(T_{n}(x)\): given a large prime number \(P\), \(T_{p}(x)\) mod \(P\) and \(T_{q}(x)\) mod \(P\), find an integer \(z\), such that_  
\[T_{z}(x)=T_{pq}(x)\) _mod \(P \tag{6}\]  
_It is difficult to solve CMDL and CMDH problems in probabilistic polynomial time._
## 3 Results and Discussion
### Cell Culture
#### 3.2.2 Cell Viability  
Utilizing fluorescent staining for cell viability assessment allows us to visually evaluate the activity status of HepaRG cells cultured on PET and PET-COL fiber scaffolds. The results presented in Figure 8 demonstrate that as the culture duration increases, PET-COL scaffolds show a gradual enhancement in green fluorescent signals, signifying live cells, in terms of both intensity and quantity. This trend suggests that PET-COL scaffolds effectively support cell growth and proliferation, thereby offering a conducive environment for cell development. Conversely, on PET scaffolds, predominantly red fluorescent signals, indicative of dead cells, are observed, with scarcely any visible green fluorescence. This finding implies that PET scaffolds offer limited support for cell growth, leading to reduced cell activity and a higher cell death rate.
# Deployment Optimization of Defense Stations in an Attack-Defense Aerial War Game
## 6 Comparisons and Discussion
In general, placing defense stations at proper sites within 40 km of the protected asset is effective for intercepting approaching attackers. A rule of thumb is to place a defense station on a line between the protected asset and an approaching attacker, close to and shorter than 40 km from the asset. Take, for example, the protection of north post on \(\zeta_{1}\) under slant formation and the south post on \(\zeta_{4}\) under pincer formation, both with kill probability of 0.7. Another rule of thumb is to place a defense station very closely behind the protected asset. Take, for example, the protection of south post on \(\zeta_{3}\) under pincer formation, with kill probability of 0.7. It is observed that, when an attacker hits its target or gets hit, the remaining interceptors in the pursuit are likely to detour against the follow-up attackers flying in the proximity. Hence, the north or south post can be well protected by placing only one defense station within 40 km of the asset, at the position that favors the interception of the first one or two approaching attackers, then the follow-up attackers will be easier to hit. Take, for example, the protection of north post on \(\zeta_{1}\) under slant formation, with kill probability of 0.7, as well as the north post on \(\zeta_{1}\), the south post on \(\zeta_{3}\) and \(\zeta_{4}\), all under pincer formation, with kill probability of 0.7. In summary, to better protect an asset, it is suggested to place a defense station on a line between the protected asset and approaching attacker, close to but shorter than 40 km from the asset. Another suggestion is to place a defense station very closely behind the protected asset to hit the approaching attackers, or place some nearby defense stations west, northwest or southwest to the protected asset to attract some assaults. More defense stations should be deployed close to the protected asset if more attackers are expected. Comparing the average hits on posts with imperfect kill probability in Table 4 and Table 5 to the hits on posts with perfect kill probability, it appears more difficult to defend the assets with imperfect kill probability. Note that the game design and the simulation results in this work are limited to aerial war games in 2D space, and the attackers and interceptors can only fly at constant speeds.
# Design and Numerical Study of Induction-Heating Graphitization Furnace Based on Graphene Coils
## 3 Results and Analysis  
### Comparison of Energy Efficiency  
#### 3.1.1 Energy Efficiency Parameters of Induction Furnaces  
The energy absorbed by the furnace charge during the heating process can be expressed as follows:  
\[\mathrm{Q_{fc}}=\int_{\mathrm{T1_{fc}}}^{\mathrm{T2_{fc}}}\mathrm{m_{fc}}\cdot \mathrm{C_{fc}dT} \tag{13}\]  
where \(\mathrm{C_{fc}}\), \(\mathrm{m_{fc}}\), \(\mathrm{T1_{fc}}\), and \(\mathrm{T2_{fc}}\) represent the specific heat capacity of the charge, the mass, and the temperatures at the beginning and end of heating, respectively. Similarly, other parts of the energy (\(\mathrm{Q_{other}}\)) can be calculated for the heating.  
The energy lost by heat dissipation (\(\mathrm{Q_{d}}\)) includes the heat dissipated by heat convection, heat conduction, and heat radiation from each component in the heating process. Part of the heat (\(\mathrm{Q_{cw}}\)) is absorbed by the cooling water, and the rest is dissipated through the furnace wall to the environment (\(\mathrm{Q_{e}}\)). In this study, the heat transfer in the furnace is simplified to solid heat transfer, and the energy absorbed by the cooling water can be expressed as follows:  
Figure 3: (**a**) Power supply curve; (**b**) comparison of core temperatures in heating period.  
Figure 2: (**a**) Global mesh structure; (**b**) detail of furnace body; (**c**) detail of coils.  
\[\mathrm{Q_{cw}}=\int_{\mathrm{t_{c1}}}^{\mathrm{t_{2}}}\frac{\lambda}{\mathrm{d}} \cdot\mathrm{S}\cdot(\mathrm{T_{t}}-\mathrm{T_{0}})\mathrm{dt} \tag{14}\]  
where \(\lambda\) is the thermal conductivity of the insulating layer, \(\mathrm{d}\) is the thickness of the insulating layer, \(\mathrm{S}\) is the heat dissipation area of the insulating layer, \(\mathrm{T_{t}}\) and \(\mathrm{T_{0}}\) are the temperature of the furnace charge at the time (t) and the temperature of the cooling water, respectively.  
Then, the total energy consumption can be expressed as follows:  
\[\mathrm{Q_{total}}=\mathrm{Q_{fc}}+\mathrm{Q_{others}}+\mathrm{Q_{d}} \tag{15}\]  
The thermal energy (\(\mathrm{Q_{gh}}\)) generated by the load (graphite heater) under the influence of a magnetic field of strength \(\mathrm{H}\) can be expressed as follows:  
\[\mathrm{Q_{gh}}=\int_{\mathrm{t_{c1}}}^{\mathrm{t_{2}}}4\pi^{2}\mathrm{\mu_{t} }\mathrm{f}\cdot\mathrm{H}\cdot\mathrm{L}\cdot\mathrm{Adt} \tag{16}\]  
where f is the current frequency, \(\mathrm{L}\) is the heater length, and \(\mathrm{A}\) is the heater cross-sectional area.  
The electrical efficiency (\(\mathrm{\eta_{e}}\)) is defined as follows:  
\[\mathrm{\eta_{e}}=\frac{\mathrm{Q_{gh}}}{\mathrm{Q_{total}}} \tag{17}\]  
The thermal efficiency (\(\mathrm{\eta_{t}}\)) is defined as follows:  
\[\mathrm{\eta_{t}}=\frac{\mathrm{Q_{fc}}}{\mathrm{Q_{gh}}} \tag{18}\]  
The energy efficiency (\(\mathrm{\eta}\)) is the product of the electrical and thermal efficiencies:  
\[\mathrm{\eta}=\mathrm{\eta_{e}}\cdot\mathrm{\eta_{t}} \tag{19}\]
# Virtual Design and Construction (VDC) Framework: A Current Review, Update and Discussion
## 3 Virtual Design and Construction (VDC)
### Project Production Management (PPM)  
The process design is defined by PPM's principles, which apply Operations Science to projects viewed as temporary production systems [56]. PPM focuses on the organization and control of project workflows and activities. One of the main applications focuses on analyzing, measuring and introducing planned buffers in these flows, which are of three types: (1) inventory buffers (raw materials, work-in-process and finished goods--transformation occurs before demand), (2) time buffers (contingency times--transformation occurs after demand) and (3) capacity buffers (in-excess labor and equipment capacity--above the average demand).  
In addition, PPM focuses on reducing workflow variability, which can be defined as a lag in the quality and quantity of flows (information, materials, labor, equipment, workspace) required to perform a sequence of constructive activities [56; 57]. The principles of PPM and Lean Construction--an adaptation of the Lean philosophy introduced by [PERSON] in 1992 [58]--share the same origin in Operations Science [35].  
Lean principles originated in the compilation of practices and values employed in the Toyota Production System. One of its main objectives is to reduce waste in a production flow and generate greater value for the client [59]. Similarly, Lean Construction aims to create production systems capable of optimizing, reducing, or eliminating workflows and improving delivery times [60].  
However, the Project Production Institute (PPI) [61]--the organization that introduced the PPM concept and practice in 2014--highlights the differences with Lean Construction. Although both concepts emerged based on Operations Science (which PPI defines as the study of the transformation of resources to create and distribute goods and services), PPI emphasizes the lack of mathematical support in the approaches of Lean Construction as well as differences in the way PPM and Lean Construction address many concepts related to systems [56].  
Within the VDC framework, PPM is placed as the process element. However, VDC has had a connection with Lean Construction long before, using tools such as the Last Planner System [45] and comparing the impact of both methodologies, such as the Lean Project Delivery System [20]. Due to this, VDC combines PPM's primary focus of activities and physical work with the human component of Lean Construction [35], making integrated use of both for optimal process design.  
Finally, PPM allows us to understand the production process as the work in the process due to its ability to model and analyze a sequence of steps/tasks to design the best project production system, with possibly less variability.
## 2 Materials and Methods
### Regional L3 and L4 SMOS Soil Moisture Data  
ESA SMOS mission was launched on 2 November 2009. SMOS is capable to produce global surface SM maps with a native spatial resolution of 35-50 km and a temporal resolution of 3 days (revisit time at the equator). Its payload is the Microwave Imaging Radiometer with Aperture Synthesis (MIRAS), an L-band interferometric radiometer composed of a Y-shaped array of 69 physically small antennas [11; 12]. SMOS data are classified in different levels with respect to the processing chain. In this study, Level 3 (L3) and Level 4 (L4) data are used.  
Daily L3 SM maps are obtained at the SMOS Barcelona Expert Center --BEC--[10] from Level 2 user data products, applying different quality filters and discarding grid points with negative SM, with a value of Data Quality Index (DQX) above a threshold, affected by Radio Frequency Interference (RFI), or with a retrieved SM data out of range. SMOS L3 data used over the three test sites in Brazil, Spain and Australia span from June 2016 to June 2017.  
L4 SM maps are obtained from the L3 SM maps by applying a downscaling algorithm to disaggregate the original SM pixels to 1 km resolution, using an improved version of the well-known empirical approach firstly developed by [PERSON] et al. [13]. This algorithm is based on the fusion of L-band brightness temperatures or low-resolution SM maps, with high resolution multi-spectral optical data (notably the NDVI), and land surface temperature. A detailed description of the methodology followed to obtain the L4 SM maps and its validation over different areas is given in [14], but the main conclusions are that the mean is preserved, with differences smaller than 0.05% and rms errors smaller than 1% with respect to the SMOS L3 product. Figure 5a,b show the sampled 1 km downscaled maps over Europe and Australia. The production of these maps is very computationally intensive and requires large amounts of memory, so in the frame of this study the downscaling has only been generated of the three specific target areas: Brazil (CEMADEM), Spain (REMEDHUS), and Australia (OzNet, in Yanco, NSW). For the first two, the data from January to December 2016 has been processed, while for Yanco it is from January to December 2015.
## 1 Introduction
Therefore, the design of the PCB and P/R equipment should be optimized to achieve the best signal [28; 29]. This paper makes two main contributions. We presented the structure and components of a complete P/R device with high flexibility that can easily be integrated into many SAM systems with different transducer types. Secondly, we presented a technique for using damping resistors to optimize unipolar pulses in order to excite high-frequency transducers, achieving a high signal-to-noise ratio. The PCB design for these signal lines is also presented in detail. The P/R device design has four channels, which makes it easy to deploy a SAM system with four transducers to reduce the scan time of a sample. This is one of the highlights when compared with similar products like the UT320 (UTEX Scientific Instruments Inc., Mississauga, ON, Canada) [30] (only one channel) or the DPR500 (Imaginant Inc., Pittsburgh, NY, USA) [22] (two channels). On the other hand, the P/R device can be expanded with eight channels. The rest of this paper is organized as follows. Section 2 is a description of the completed design of the P/R device. The implementation of the hardware and code is introduced in Section 3. The detailed setup for testing and the results are presented in Section 4. Finally, a concluding remark is given in Section 5.
## 4 Experimental Analysis  
In this paper, the analysis of performance is carried out on the publicly available datasets MovieLens 1M and Yahoo Music, and the details of the two datasets are shown in Table 1. To better observe the experimental effect, MovieLens 1M and Yahoo Music are both divided into the training set and the test set in the ratio of 8:2.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** & \begin{tabular}{c} **Number of** \\ **Users** \\ \end{tabular} & \begin{tabular}{c} **Number of** \\ **Items** \\ \end{tabular} & \
\begin{tabular}{c} **Number of** \\ **Ratings** \\ \end{tabular} &
\begin{tabular}{c} **Range of** \\ **Ratings** \\ \end{tabular} & **Sparsity (\%)** \\ \hline MovieLens & 6000 & 4000 & 1,000,000 & \{1,2,3,4,5\} & 95.83 \\ Yahoo Music & 8089 & 1000 & 270,121 & \{1,2,3,4,5\} & 96.66 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Basic information on datasets.  
Here, we use the mean absolute error (_MAE_) and the root mean square error (_RMSE_) as the evaluation indicators, and detailed definitions of _MAE_ and _RMSE_ are:  
\[MAE=\frac{\sum_{u\in UL,v\in V}\lvert r_{uv}-r^{\prime}_{uv}\rvert}{n} \tag{12}\]  
\[\text{RMSE}=\sqrt{\frac{\sum_{u\in UL,v\in V}(r_{uv}-r^{\prime}_{uv})^{2}}{N}} \tag{13}\]  
where \(r_{uv}\) and \(r^{\prime}_{uv}\) represent the real rating and the predictive rating of user, \(u\), on item, \(v\), respectively. \(U\) and \(V\) represent the collection of users and the collection of items, respectively.
## 5 Addressing the Research Questions
### RQ1: What Are the Primary Fields of Application Where VR, AI, and IoT Are Used in Combination?
#### 5.3.1 Digital Twins  
The first and most common approach across all the articles incorporating all the aforementioned technologies are digital twins. DTs are a prevalent methodology providing realism since they serve as virtual replicas of physical systems, allowing real-time monitoring and control through IoT devices, predictive analysis through AI models, andimmersive visualization through VR interfaces [86]. This approach is commonly employed in industries such as healthcare, manufacturing, and smart cities.