esa-sceva/satcom-chunk-collection huggingface dataset:
# GNSS-Based Vertically Monitoring of Super-Tall Buildings
## 5 Verticality Monitoring Result and Analysis of the W Tower
### Horizontal Motion Analysis of the W Tower
\begin{table}
\begin{tabular}{c c c c c c c} \hline \multicolumn{6}{c}{\(xyH\)**Directions (without Processing)} & \multicolumn{2}{c}{\(xyH\)**Directions (after Processing)} \\ \hline  & \(V_{x}\) & \(V_{y}\) & \(V_{H}\) & \(V_{x}\) & \(V_{y}\) & \(V_{H}\) \\ \hline \(<\)1 cm & 76.14 & 82.39 & 40.05 & 76.63 & 84.05 & 41.50 \\
1–2 cm cm & 20.18 & 15.68 & 27.31 & 19.85 & 14.52 & 27.27 \\
2–3 cm & 3.03 & 1.54 & 14.48 & 2.98 & 1.28 & 14.91 \\
3–4 cm & 0.60 & 0.28 & 7.56 & 0.54 & 0.15 & 6.81 \\ \(\geq\)4 cm & 0.05 & 0.11 & 10.60 & 0 & 0 & 9.51 \\ \hline \end{tabular}
\end{table} 
Table 6: Statistics of differences in position in relation to the coordinate time series of the monitoring point (%). Figure 7: Differences in position in relation to the coordinate time series of the monitoring point: (**a**) \(x\) direction; (**b**) \(y\) direction; (**c**) \(H\) direction.
###### Abstract  
Industrial technologies for processing tungsten concentrates using soda roasting or autoclave leaching are based on the production of alkaline sodium tungstate solutions that contain impurities such as silicon, phosphorus, arsenic, and others. The purification of these solutions from impurities requires the neutralization of excess soda or alkali with inorganic acids, which leads to the formation of chloride and sulfate effluents that are subsequently discharged into waste repositories. An analysis was carried out on existing methods for the production and processing of sodium tungstate solutions using HNO\({}_{3}\) and NH\({}_{3}\), as well as extraction and sorption techniques involving anion exchange resins. Currently, processes such as nanofiltration, reverse osmosis, and electrodialysis are being applied for water purification and the treatment of sulfate and chloride effluents. These processes employ various types of industrially manufactured membranes. For the purpose of electrodialysis, a two-compartment electrodialyzer setup was employed using cation-exchange membranes of the MK-40 (Russia) and EDC1R (China) types. The composition and structure of sodium tungstate, used as the starting reagents, were analyzed. Based on experiments conducted on a laboratory-scale unit with continuous circulation of the catholyte and anolyte, dependencies of various parameters on current density and process duration were established. Stepwise changes in the anolyte pH were recorded, indirectly confirming changes in the composition of the Na\({}_{2}\)WO\({}_{4}\) solution, including the formation of polytungstates of variable composition and the production of H\({}_{2}\)WO\({}_{4}\) via electrodialysis at pH < 2. The resulting tungstic acid solutions were also analyzed. The conducted studies on the processing of sodium tungstate solutions using electrodialysis made it possible to obtain alkaline solutions and tungstic acid at a current density of 500-1500 A/m\({}^{2}\), without the use of acid for neutralization. Yellow tungstic acid was obtained from the tungstic acid solution by evaporation.  
sodium tungstate; tungstic acid; purification; electrodialysis; membrane processes
## 5 Discussion  
The present research aims to provide empirical evidence of the positive effect of EO on the adoption of ITCs and the positive effect of both constructs on FP. Additionally, the mediating role of ITC in the relationship between EO and FP is analyzed. For this purpose, the DCV was adopted as the theoretical framework, which is widely used in strategic management and particularly useful in understanding the role of IT in creating business value. The proposed objectives were successfully met; the main findings of the study are discussed below.  
Through the DCV approach, the proposed hypotheses were validated, confirming that dynamic capabilities have positive effects in moderately dynamic environments [71], as is the specific case of Chile. Firstly, it was found that EO has a direct and positive effect on firm FP (H1 accepted). This result aligns with previous meta-analyses that demonstrated a strong association between EO and FP [17,18]. The findings suggest that companies adopting a strategic stance based on three dimensions from [PERSON] [39]/[PERSON] and [PERSON] [40]--risk-taking, proactivity, and innovation--tend to perform better, supporting previous literature such as [PERSON] et al. [42] and [PERSON] [43].  
Secondly, it was found that EO has a direct and positive effect on the development of ITC (H2 accepted), which aligns with the perspective that EO is a vital management capability within the domains of technology [1]. The results confirm that EO promotes the proactive search for technological innovations and the pursuit of market opportunities related to IT that can potentially bring substantial returns [72]. This also aligns with the findings of [PERSON] et al. [20], who were pioneers in providing empirical evidence of the positive effect of EO on ITC.  
Thirdly, it was demonstrated that ITC has a direct and positive effect on FP (H3 accepted), validating that ITC can help companies outperform their competitors by enabling timely access to information and knowledge [20]. These findings align with previous research by [PERSON] et al. [49], [PERSON] et al. [20], and [PERSON] et al. [5].  
Lastly, the findings show that ITC mediates the relationship between EO and FP (H4 accepted), suggesting that EO influences the development of ITC, which in turn positively affects FP. The partial complementary mediation indicates that ITC plays a key intermediary role as a bridge, amplifying the positive effects of EO, but EO also influences FP independently and possibly through other mechanisms. It is important to note that previous research, such as that conducted by [PERSON] et al. [20], has analyzed how ITC and knowledge management together have a mediating effect on the EO-FP relationship. However, to the best of our knowledge, this is the first time that the specific mediating effect of ITC in this relationship has been independently analyzed. This distinction corroborates the vital importance of combining EO and ITC to enhance FP. In line with the findings of [PERSON] et al., our results suggest that firms with a strong EO can enhance their performance by effectively developing their own ITC capabilities [20].
# On the Micromixing Behavior of a Spinning Disk Reactor for Metallic Cu Nanoparticles Production
###### Abstract  
The interest in producing copper nanoparticles is high, mainly due to their enhanced properties, a wide range of possible and different applications, and the possibility for their use in the framework of catalysis. The purpose of this study is to synthesize copper nanoparticles by chemical reduction of copper sulfate with L-ascorbic acid and sodium borohydride capable to eliminate free radicals providing an antioxidant environment promoting pure copper nuclei formation. The micromixing conditions necessary for the nanoparticles production are provided by a spinning disk reactor (SDR). Relevant operational parameters, such as the disk speed velocity and the position of the reactant injectors, will lead to different product outcomes. The latter was checked by means of a dynamic light scattering instrument (DLS). At the end, depending on the adopted operating conditions, the SDR was able to produce particles between 16 nm and 39 nm, with a particle size distribution (PSD) characterized by a narrow, monomodal plot. In comparison to the smallest particles obtained in a stirred reactor tank, that is, 132 nm, the obtained results appear to be very promising.  
copper nanoparticles; spinning disk reactor; catalyst application Article
# GIS and BIM as Integrated Digital Environments for Modeling and Monitoring of Historic Buildings
## 2 Materials and Methods
### GIS Platform  
The results from the 3D surveying and the multidisciplinary documentation were further processed in order to be compatible with spatial information system, so that mapping of the building materials as well as deterioration of the building (e.g., cracks, deformations, voids) can be possible. Within a GIS platform, 2D and 3D GIS operations were utilized, to model, document and analyze the building materials and the decay extent regarding the structural integrity of the building. Throughgeo-processing analysis, within the GIS software, thematic maps of building materials, decay patterns and extent, deformations et al., were depicted and presented. The extent of decay in terms of cracks, was analyzed taking into consideration the relevant information regarding physical-chemical analyses and visual inspection of the structure. The multidisciplinary data were mapped within a Computer Aided Design (CAD) environment, and more specifically in AutoCAD Map 3D (AutoCAD Map 3D, Autodesk, San Rafael, CA, USA, 2013), imported in ArcMap (ArcMap 10.5.1, ESRI, Redlands, CA, USA, 2017) as feature classes and through a process of topology building and thorough geo-processing analysis, thematic maps of the types of cracks and detachment of building materials were produced. The topology building was processed within ArcMap, where spatial relationships among features of the same layer were created, and facilitated for the development of thematic maps [(20; 21)]. In addition, further analysis and classification was performed providing information regarding the extent of the damage of the structure according to the construction phase depicted.
## 3 Methodology
### Model Selection  
To assess the model fit and compare different model specifications, we rely on key statistical indicators, particularly the log-likelihood value (\(LL\)), the Bayesian Information Criterion (\(BIC\)) [46]. The \(LL\) value measures how well the model explains the observed choices: a higher (less negative) \(LL\) indicates better in-sample fit. However, as model complexity increases, the \(LL\) alone may favor overfitted models. Therefore, the \(BIC\) is reported additionally, which penalizes model complexity and helps in selecting a more parsimonious specification. The \(BIC\) is defined as  
\[BIC=-2\cdot LL+k\cdot\ln(N) \tag{8}\]  
where \(k\) denotes the number of estimated parameters and \(N\) represents the number of observations. Lower \(BIC\) values indicate a better trade-off between model fit and complexity.
## 1 Introduction  
New emerging technologies in the context of Industry 4.0, such as digital twins, pose new challenges in the design and simulation in the industrial and biomedical ecosystems. The interactive nature of the processes of Industry 4.0 requires fast simulation methods that enable real-time decision making and digital twins being continuously updated with the physical world [1].  
Lattice materials have multiple applications in engineering (e.g., energy absorption) and biomedicine (e.g., implants and scaffolds) [2]. However, the simulation of large lattice domains is in many cases unfeasible because: (1) the meshing of these domains is a time consuming process that involves human intervention and (2) the size of the produced meshes is intractable due to the geometric complexity associated with these domains [3,4].  
This manuscript implements a methodology that combines material homogenization and design of experiments (DOE) to estimate the stress-strain response in large lattice domains. The main advantage of this methodology is its lesser computational demands in comparison to finite element analysis (FEA). We apply this approach to approximate the von Mises stress in lattice structures of the type Schwarz primitive. This manuscript is an extension of the work in [5] which presents a methodology to develop the meta-models using DOE but does not integrate them with material homogenization to produce stress-strain estimations in large lattice domains.  
The remainder of this article is organized as follows: Section 2 provides a review of the relevant related work. Section 3 describes the proposed methodology to estimate the stress-strain response in lattice domains using material homogenization and DOE. Section 4 presents and evaluates the results of the implementation of our methodology. Section 5 concludes the manuscript and suggests future extensions of the article.
## 2 Literature Review  
### Internet of Things  
The IoT can be defined as \"a worldwide network of interconnected objects uniquely addressable, based on standard communication protocols\" ([15], p. 685). A more comprehensive description characterizes IoT as \"an ecosystem that contains smart objects equipped with sensors, networking and processing technologies integrating and working together to provide an environment in which smart services are taken to the end users\" ([16], p. 241). The IoT finds widespread application across various domains, with predictions indicating that by 2030, there will be 30 billion IoT devices deployed worldwide [17]. These ubiquitous IoT devices form an interconnected structure resembling the future Internet, influencing not only cities but also the world at large [18]. Smart cities, energy conservation, pollution control, smart homes, emission reduction, intelligent transportation, and the smart industry have all experienced transformations through the integration of IoT [19].  
The architecture of the IoT mainly consists of three layers: the perception layer, the network layer, and the application layer [20]. The perception layer, as the foundational level of IoT, comprises physical devices such as sensors that collect data and transmit it to the network layer [19]. The network layer manages the reception and transportation of substantial volumes of sensor-produced data to the application layer through various communication technologies and protocols, facilitated by gateways [21]. At the application layer, data received from the network layer is stored in databases, and various tools are employed for data analysis, leading to the generation of diverse applications related to smart cities, smart health, and other fields [13].
# Performance Evaluation of IM/DD FSO Communication System Under Dust Storm Conditions
## 4 Results and Discussion
### Outage Probability Simulation Results  
Figure 4 illustrates the outage probability as a function of the average SNR, where the threshold SNR is set to \(\gamma_{\rm th}=1\) dB. The results show a consistent exponential decay in outage probability with the increase in the SNR, confirming the system's improved robustness at higher power levels. Notably, the exact analytical expression, the closed-form approximation, and the Monte Carlo simulations exhibit excellent agreement across the entire SNR range. This perfect match justifies the accuracy and the tightness of the derived approximated expression for the outage probability.  
The observed exponential decay in outage probability with the increase in the SNR implies a high sensitivity of outage probability to SNR improvements, meaning minor enhancements in the SNR can significantly reduce the likelihood of communication failure.  
Like the BER threshold defined by FEC limits, outage probability follows practical design constraints. A typical target in reliable communication systems is an outage probability below \(10^{-3}\), ensuring that the link remains available for at least 99.9% of the time under average channel conditions. For instance, with an SNR threshold of 1 dB, achieving an average SNR of just 10 dB results in a \(10^{-3}\) outage probability--effectively indicating 99.9% link availability. This finding is particularly valuable for planning FSO links and other delay-sensitive applications where even short outages may be detrimental.  
Figure 4: Outage probability versus the system SNR for a threshold value of \(\gamma_{\rm th}=1\) dB.  
Figure 3: Average BER performance versus the system SNR.
## 4 In-Network Computing via ICN  
A variety of studies have explored NDN as a promising enabler for in-network computing, primarily focusing on generic services where processing is confined to a single in-network executor.  
In this section, we review the most notable approaches, structuring our analysis around the following critical deployment steps: (i) service naming schemes and communication messages, (ii) reactive and (iii) proactive strategies for selecting in-network executors, (iv) techniques for compute reuse, and (v) security considerations.
## 9 Major Components of Proposed Virtualization Model
The components like service discovery and resource discovery decouple the infrastructure from the services and applications. Where services and infrastructure are shared, authentication plays a vital role. Similarly, in virtualized federated sensor networks, authentication manager makes sure the services and infrastructure provided should be accessed by the authenticated users and applications. The sensor device at home and the health care data in particular, should not be accessed by the unauthenticated user. The virtualization manager is the brain of the middleware. It collaborates with energy manager, communication manager, resource discovery module, service discovery module and the registry to serve the user queries and application demands. Furthermore, it makes sure the application demands are met in an energy efficient manner. It resides at both the node and the gateway. The energy manager module takes the power parameters from different sensor nodes and manages the power of the network; it collaborates with the virtualization manager such that the nodes with more power are to be utilized intermittently, so as to increase the network longevity. Communication manager is responsible for selecting the radios and the channels for communication among the nodes and the gateway. Application based radio selection makes sure that the application demands are fulfilled. The flexibility of switching between different radios provides high flexibility in terms of data rate and communication range. However, an eye has to be kept on the power consumption of transmission. The middleware part that resides at the node consists of neighbor service discovery that keeps the record of the single hop neighbors' location, services they offer and the resources they have. Device manager module manages the sensor node hardware. It updates the virtualization manager about the malfunctioning, damage or tempering of any sensor attached to the node. The device manager and the energy manager modules enable nodes to update the gateway with their current state of power, radios and services. The node virtualization manager is connected to device manager, registry, energy manager and communication manager that allow virtual manager to manage all the node operations. The gateway's virtualization manager along with its interfaces with the communication manager and registry tries to compute the best possible combination of clusters that satisfy the QoS demanded by the application. Furthermore, it also tries to accommodate the error and the delay in transmission of the data. Moreover, it tries to balance the load of traffic on the links and sensor nodes. The service discovery module along with the registry aggregates the data.
## 2 Materials and Methods
### Pyrolysis Processes  
The pyrolysis study was developed on a fixed bed cylindrical reactor inserted in an electric furnace, using an initial amount of 15 g of pristine biomass. Detailed technical characteristics and dimensions have been described elsewhere [18].  
Briefly, the reactor, made of **stainless steel**, has an inner diameter of 4 cm (outer diameter of 4.3 cm) and a total height of 75 cm, It is provided with a stainless-steel basket, in which the sample was placed.  
Nitrogen gas (N\({}_{2}\), 100 mL min\({}^{-1}\)) was used as a carrier gas, introduced through a lateral inlet, while the gaseous products exited through a top outlet connected to a cooling system. This setup allowed for the condensation of high-molecular-weight gases.  
Pyrolysis experiments were conducted at three temperature levels (500 \({}^{\circ}\)C, 600 \({}^{\circ}\)C, and 700 \({}^{\circ}\)C) for a constant reaction time of one hour, based on prior experimental optimizations. During the heating phase, gases were continuously routed through a sampling system consisting of a Tesla bag for periodic collection and analysis of the gas composition under the tested conditions. Nitrogen (N\({}_{2}\)) was used as the carrier gas at a fixed flow rate of 100 mL/min. No variations in gas flow rate or reaction time were tested, ensuring that temperature was the only variable parameter in this study. After the heating period, the reactor was cooled under a nitrogen flow before the solid phase was removed.  
At the end of the heating period, the reactor was cooled by maintaining a flow of nitrogen until it returned to room temperature. The stainless-steel basket was then lifted to remove the solid phase. The solid char yield was determined gravimetrically, and its higher heating value (HHV) was measured using a bomb calorimeter (Parr 1351) following the ISO 1928 standard [25].  
The HHV of the gaseous phase was estimated based on its chemical composition, which was analyzed using gas chromatography.  
Each pyrolysis experiment was conducted in triplicate at 500 \({}^{\circ}\)C, 600 \({}^{\circ}\)C, and 700 \({}^{\circ}\)C using identical operational conditions, with the results averaged and deviations reported where applicable.