esa-sceva/satcom-chunk-collection huggingface dataset:
# A Reference Model for Monitoring IoT WSN-Based Applications
## 1 Introduction
From the authors' point of view, this situation is similar to those of computer networks, which began as a set of ad-hoc systems that did not follow any standard. The spectacular advances achieved in this field had been partially possible because of the adoption of a universally accepted architecture that promoted the use of standards. Following this approach, many researchers and standardization organisms have promoted the standardization of WSNs in the last years [6; 7; 8; 9; 10]. Now, we advocate for the standardization of WSN Monitoring Platforms (WSN-MPs). Considering the example of computer networks, the aim of this paper is to propose a new reference model for WSN-MPs, to establish a systematic methodology for the analysis, design, implementation and operation of monitoring platforms on sensor networks. This model allows the comparative analysis of WSN-MPs, and offers a common environment for the design and standardization of new platforms [5]. It is based on decomposing the monitoring process into different layers, identifying the responsibilities and establishing the services, protocols and interfaces needed to accomplish them. This approach offers several new interesting features, such as flexibility (adaptable to any type of system or application), simplicity (due to layer division), universality (foreseeing any system), and adaptability (able to follow the future evolution of WSNs and their WNS-MPs). This model could serve as first approach to achieve a standardization of WSN-MPs. If WSN-MPs' design and implementation were to follow a standard, a rigorous and systematic approach to the characteristics of WSN would be possible, as far as it can be granted that the WSN-MP used when analyzing that both WSNs have the same properties, and thus certify their operation. This point becomes critical for IoT applications as data producers and consumers are decoupled. On the other hand, the implementation of new WSN-MPs may be simplified, building new monitoringplatforms by combining off-the-shelf modules. Finally, a new standard reference model which has to be detailed also promotes new research areas to complete and develop the issues pointed in this article. This paper is structured as follows: after this Introduction, Section 2 provides a review of the monitoring tools developed by the scientific community. Section 3 outlines the research design and methods used to achieve the main result of this work. This result, the proposed model, is presented in Section 4. As a validation phase of the presented model, Section 5 applies the model to the characterization of the WSN-MPs detailed in Section 2. Section 5 also uses the model to design a new WSN-MP. Finally, Section 6 summarizes the conclusions of this work.
# Shedding Light on FIRE Syndrome: An Overview of a Novel Condition in Eosinophilic Esophagitis
## 2 Reported Cases of FIRE Syndrome: Insights from the Literature  
### Methods  
FIRE syndrome has recently been documented as a distinct clinical entity in EoE patients [16]. Consequently, data on this subgroup remain limited. Several cases have been described in the literature, providing insights into its symptomatology, triggers, and impact. The aim of this paper is to review the available literature data on FIRE syndrome and to propose some hypotheses regarding its pathophysiology and management strategies.  
To support this aim, a literature search was conducted to identify and summarize relevant data on FIRE syndrome published before 1 February 2025. Relevant studies were identified through a search in three major databases: PubMed, Embase, and Web of Science. We used Medical Subject Heading (MeSH) and keywords including \"food-induced immediate response of the esophagus\", \"FIRE syndrome\", \"eosinophilic esophagitis\", and \"esophageal immediate hypersensitivity\". Boolean operators (AND/OR) were applied to refine the search strategy. Additionally, we manually reviewed the reference lists of all included studies to identify further relevant publications. Conference abstracts, studies unrelated to the scope of this narrative review, and articles not available in English were excluded. Databases such as Scopus and Google Scholar were not included, as they were found to have substantial content overlap with the sources consulted.  
The search yielded 24 initial entries. After removing duplicate records and screening for relevance based on titles and abstracts, a total of 4 papers were included for review. These comprised two original articles, one case series, and one case report [16; 17; 18; 19]. Study selection and data synthesis were conducted by two authors as part of this narrative review. For transparency reasons, the article selection process is illustrated in Figure 1.  
The current evidence base is limited by the low number of published studies, all of which implemented observational or descriptive designs. This poses essential limitations in terms of methodological rigor, generalizability and strength of the conclusions. However, as these reports represent the only available data on FIRE syndrome, we aimed to critically evaluate and synthesize them in order to highlight the key characteristics and identify knowledge gaps for future research.
# Development of a Path Tracker Based on a 4 WS Vehicle for Low-Speed Automated Driving Systems
## 2 4 WS Vehicle Model  
In this paper, the 4 WS system is used to improve the path-tracking performance of low-speed autonomous vehicles. While previous studies utilizing four-wheel steering systems have primarily focused on enhancing vehicle stability in high-speed driving environments, requiring vehicle dynamic parameter values, this paper employs a kinematic model of the vehicle, as it is necessary to analyze the system in a low-speed driving environment [9; 10; 11; 12].
# Additively Manufactured Inconel 718 Low-Cycle Fatigue Performance
###### Abstract  
Inconel 718 is one of the most used alloys within the aerospace gas turbine industry. The acceptance of Inconel 718 within the aerospace gas turbine industry has largely been due to its high strength and fatigue capabilities up to 677 \({}^{\circ}\)C (1250 \({}^{\circ}\)F). This alloy is traditionally produced through conventional manufacturing methods, such as casting, wrought, and sheet forming. The various traditional manufacturing methods of this alloy have been well understood and characterized for use in critical components. However, Inconel 718 can also be produced with non-traditional manufacturing methods, such as by additive manufacturing. Producing Inconel 718 by additive manufacturing has the opportunity to design more complex components that provide distinct advantages over conventionally produced components. However, prior to implementing additively manufactured Inconel 718 within the aerospace gas turbine industry, there needs to be a complete understanding of the material's performance. In an effort to completely characterize additively manufactured Inconel 718, this study focuses on the characterization of the alloy's low-cycle fatigue performance. Specimens were produced via the laser powder bed fusion process in a vertical orientation. Both as-printed surfaces and fully machined surface specimens were evaluated at 24 \({}^{\circ}\)C (75 \({}^{\circ}\)F) and 538 \({}^{\circ}\)C (1000 \({}^{\circ}\)F). Fractography analysis was then completed on the specimens to understand differences in the crack initiation and propagation with respect to test temperatures and surface conditions. Based on these tests, it was shown that the fatigue life knockdown due to the as-printed surface conditions was 62.8% at 538 \({}^{\circ}\)C (1000 \({}^{\circ}\)F) versus only 8.5% at 24 \({}^{\circ}\)C (75 \({}^{\circ}\)F). These findings are discussed in detail within this article, and future work is proposed.  
laser powder bed fusion; Inconel 718; low cycle fatigue 2025  
15, 1653
# Deep Soft Rock Tunnel Perimeter Rock Control Technology and Research
## 3 Engineering Application of Roadway Support Technology
### Numerical Modeling  
Based on the coal seam structure of the 2500 transportation roadway, the support structure is mainly made of materials, such as an anchor cable, anchor rods, steel bands, trays, and metal mesh. FLAC\({}^{\text{3D}}\) numerical simulation software (FLAC3D6.00) is used to analyze and study the problem and to establish a numerical analysis model to simulate the deformation of the surrounding rock under this support scheme. As shown in Figure 9, the model size is length \(\times\) width \(\times\) height = 50 m \(\times\) 40 m \(\times\) 45 m, and a total of 100,942 units are divided into the model, adopting the Moore-Cullen yield criterion. The model's boundary conditions are rigid constraints at the bottom, with surrounding constraint horizontal displacement and a top stress boundary. The specific boundary conditions are shown in Figure 10.  
Figure 8: Scan of the sandstone specimen.  
Figure 7: Scan of the mudstone specimen.  
The buried depth of the roadway is 480 m. According to the ground stress calculation formula, the vertical stress of the roadway is 8.028 Mpa, and the pressure measurement coefficient is taken as 1. This stress is applied to the top and the gang part of the model, and the vertical and horizontal stresses are shown in Figure 11. The plastic zone is shown in Figure 12. The calculation results of the vertical and horizontal displacements of the roadway in the support scheme are shown in Figures 13 and 14.  
Figure 10: Numerical model boundary conditions.  
Figure 9: Numerical analysis models.  
Figure 11: Vertical and horizontal stress diagrams applied.  
Figure 12: Plastic zone of the surrounding rocks.  
Figure 13: Numerical simulation of the vertical deformation.  
Through the numerical simulation results, it can be seen that under this support scheme, the maximum values of roof subsidence and bottom bulge are 66.3 mm and 120 mm, respectively, while the total displacement is about 180 mm, and the displacement of the two gangs is about 165 mm. In summary, the numerical analysis results show that the support program can effectively control the deformation of the tunnel perimeter rock.
# Taylor Bird Swarm Algorithm Based on Deep Belief Network for Heart Disease Diagnosis
## 5 Conclusions
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Dataset** & **Methods** & **Accuracy** & **Mean** & **Variance** & **Sensitivity** & **Mean** & **Variance** & **Specificity** & **Mean** & **Variance** \\ \hline \hline \multirow{4}{*}{Cleveland} & SVM & 0.754 & 0.752 & 0.002 & 0.758 & 0.754 & 0.004 & 0.750 & 0.748 & 0.002 \\ \cline{2-11}  & NB & 0.765 & 0.761 & 0.004 & 0.767 & 0.765 & 0.002 & 0.758 & 0.754 & 0.004 \\ \cline{2-11}  & DBN & 0.774 & 0.771 & 0.003 & 0.771 & 0.768 & 0.003 & 0.781 & 0.779 & 0.002 \\ \cline{2-11}  & Proposed Method & 0.871 & 0.869 & 0.002 & 0.879 & 0.878 & 0.001 & 0.862 & 0.860 & 0.002 \\ \hline \multirow{4}{*}{Hungarian} & SVM & 0.696 & 0.693 & 0.003 & 0.750 & 0.748 & 0.002 & 0.611 & 0.608 & 0.003 \\ \cline{2-11}  & NB & 0.750 & 0.746 & 0.004 & 0.800 & 0.799 & 0.001 & 0.667 & 0.665 & 0.002 \\ \cline{2-11}  & DBN & 0.765 & 0.763 & 0.002 & 0.813 & 0.810 & 0.003 & 0.684 & 0.682 & 0.002 \\ \cline{2-11}  & Proposed Method & 0.913 & 0.911 & 0.002 & 0.933 & 0.932 & 0.001 & 0.875 & 0.873 & 0.002 \\ \hline \multirow{4}{*}{Switzerland} & SVM & 0.677 & 0.675 & 0.002 & 0.688 & 0.684 & 0.004 & 0.667 & 0.665 & 0.002 \\ \cline{2-11}  & NB & 0.704 & 0.702 & 0.003 & 0.692 & 0.690 & 0.002 & 0.714 & 0.711 & 0.003 \\ \cline{2-11}  & DBN & 0.714 & 0.711 & 0.003 & 0.714 & 0.713 & 0.001 & 0.714 & 0.712 & 0.002 \\ \cline{2-11}  & Proposed Method & 0.846 & 0.844 & 0.002 & 0.857 & 0.855 & 0.002 & 0.833 & 0.831 & 0.002 \\ \hline \multirow{4}{*}{Cleveland} & SVM & 0.776 & 0.773 & 0.003 & 0.823 & 0.822 & 0.001 & 0.694 & 0.691 & 0.003 \\ \cline{2-11}  & NB & 0.830 & 0.826 & 0.004 & 0.867 & 0.865 & 0.002 & 0.765 & 0.761 & 0.004 \\ \cline{2-11}  & DBN & 0.897 & 0.895 & 0.002 & 0.913 & 0.911 & 0.002 & 0.875 & 0.873 & 0.002 \\ \cline{2-11}  & Proposed Method & 0.934 & 0.932 & 0.002 & 0.950 & 0.948 & 0.002 & 0.903 & 0.901 & 0.002 \\ \hline \multirow{4}{*}{Hungarian} & SVM & 0.746 & 0.743 & 0.003 & 0.742 & 0.740 & 0.002 & 0.750 & 0.748 & 0.002 \\ \cline{2-11}  & NB & 0.791 & 0.790 & 0.001 & 0.800 & 0.797 & 0.003 & 0.781 & 0.780 & 0.001 \\ \cline{2-11}  & DBN & 0.871 & 0.868 & 0.003 & 0.879 & 0.878 & 0.001 & 0.862 & 0.860 & 0.002 \\ \cline{2-11}  & Proposed Method & 0.902 & 0.900 & 0.002 & 0.909 & 0.907 & 0.002 & 0.893 & 0.891 & 0.002 \\ \hline \multirow{4}{*}{Switzerland} & SVM & 0.679 & 0.677 & 0.002 & 0.679 & 0.675 & 0.004 & 0.679 & 0.677 & 0.002 \\ \cline{2-11}  & NB & 0.755 & 0.752 & 0.003 & 0.750 & 0.748 & 0.002 & 0.760 & 0.758 & 0.002 \\ \cline{1-1} \cline{2-11}  & DBN & 0.820 & 0.818 & 0.002 & 0.830 & 0.827 & 0.003 & 0.820 & 0.819 & 0.001 \\ \cline{1-1} \cline{2-11}  & Proposed Method & 0.840 & 0.838 & 0.002 & 0.846 & 0.844 & 0.002 & 0.833 & 0.832 & 0.001 \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Statistical Analysis. writing--review and editing, A.M.A. and W.M.N.W.Z. ; visualization, A.M.A. and W.M.N.W.Z. ; supervision, W.M.N.W.Z. All authors have read and agreed to the published version of the manuscript. This research received no external funding. The authors declare no conflict of interest.
## 5 Long-Short-Range Communication
### Short-Range Communication  
In healthcare systems, short-range technology is used in wearable devices for short-range communication among nodes, especially between the master node and sensor nodes, where data processing occurs. Short-range standards are used for different applications such as smart lighting in mesh networks. Here, we discuss the development of a small wideband network consisting of only a central node and a few sensors. Many short-range standards are available, the most well-known of which for IoT applications are Zigbee and low-power Bluetooth. The features of some standards used in short-range communication are listed in Table 2.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Zigbee** & **Bluetooth** \\ \hline Operating band & 2.5 GHz & 2400–2484 MHz \\ \hline Topography & Mesh topology & Star topology \\ \hline Radius & 10–100 m & 30 feet \\ \hline Rate of data & 250 kbps & 24 Mbps \\ \hline \multirow{4}{*}{Safety attribute} & A network with an encryption & Pairing is implemented in the \\  & key (128-AES) and a link key is & secure mode to exchange \\ \cline{1-1}  & available to provide additional & information and provide \\ \cline{1-1}  & security in the application layer & two-key authentication and \\ \cline{1-1}  & & protection \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of short-distance communication standards.
## 4 Discussion
### Parameter Estimation and Model Evaluation
The relationship between SIF and GPP is sometimes complex and ecosystem-specific [4], and SIF contains information on canopy physiological processes [67]. The ground area of OCO-2 SIF soundings is much closer to the flux tower footprint, and several recent studies showed linear relationship between the OCO-2 SIF and flux tower GPP (e.g., [2; 5; 14; 44]). Meanwhile, modeling studies have also demonstrated the linear SIF-GPP relationship using processed-based models like SCOPE. Similar to [44] and [20], we found strong linear correlation between SIF and GPP from both observations and models. Our results also showed that the SIF simulated by our optimized SCOPE model could be used to estimate GPP fairly well based on the SIF-GPP relationship established from satellite-derived SIF data and flux tower GPP. The long-term SIF simulations and the resulting GPP estimates can be used to examine the dynamics of terrestrial photosynthesis at various timescales.
## 3 Results and Discussion
### Average Received Optical Power  
Figure 5 shows the average results of the optical power simulation received for the proposed CSK-based UM-VLC channel model and compares it to a reference channel model consisting of a single monochromatic LED and a single PD with an optical filter gain equal to unity, i.e., \(\mathcal{G}_{\text{ref}}=1\), and a wall reflectance of \(\rho_{\text{ref}}=0.6\). In Figure 5, the total average optical power received (i.e., the sum of the average optical power received at the red, green, and blue PD) of the LOS link is shown over the simulation space for the proposed model, where the impact of shadowing is clearly manifested. In Figure 5, the total average optical power received from the NLOS link is shown for the proposed model, where it is clearly observed how the two tunnel walls contribute to the received power in different ways.  
The wall at \(y=0\) is much closer to TX, and thus the closer RX is to the said wall, the more NLOS power it receives. It is also worth noting that, as should be expected, the shadowing effect is much less severe than in the LOS case. Figure 5c shows the total optical power received for the proposed model, where it can be seen how the NLOS link supplements the LOS link when the latter is shadowed, making the shadowing effect not as pronounced as in Figure 5a.  
Figure 5d graphs the ratio between the average received optical power for the reference model and for the proposed model. It can be seen that the proposed model receives a much  
Figure 5: Optical power at RX. (**a**) Total average received power from LOS link (proposed model). (**b**) Total average received power from NLOS link (proposed model). (**c**) Total average received power (proposed model). (**d**) Ratio of total average received power between the reference model and the proposed model. (**e**) Empirical CDF for average received power at red, green, and blue PD (proposed model). (**f**) Empirical CDF for total average received power, comparison between the proposed model and reference model.  
lower optical power than the reference model, and this difference between the two models tends to increase the further away from the LED the RX is; this is a direct result of the dust attenuation that is factored into the proposed model, but not in the reference model. In the shadowed positions (where NLOS dominates over LOS), the difference between the two models is greater. This is explained by the fact that the NLOS link is much more affected by the dust attenuation than the LOS link, since the NLOS link tends to be longer (and thus the attenuation coefficient is larger). Figure 5e presents the empirical cumulative distribution function (ECDF) for the received optical power at the red, green, and blue PDs for the proposed model, whereas Figure 5f presents the ECDF for the total received optical power for the proposed and reference models.
###### Abstract  
Experiments were performed on four specimens of steel frames with infilled recycled aggregate concrete shear walls (SFIRACSWs), one specimen of infilled ordinary concrete wall, and one pure-steel frame were conducted under horizontal low cyclic loading. The influence of the composite forms of steel frames and RACSWs (namely, infilled cast-in-place and infilled prefabricated) on the failure modes, transfer mechanisms of lateral force, bearing capacity, and ductility of SFIRACSWs is discussed, and the concrete type and connecting stiffness of beam-column joints (BCJs) are also considered. Test results showed that infilled RACSWs can increase the bearing capacity and lateral stiffness of SFIRACSWs. The connecting stiffness of BCJs slightly influences the seismic behavior of SFIRACSWs. In the infilled cast-in-place RACSWs, the wall cracks mainly extended along the diagonal direction. The bearing capacity was 2.4 times higher than in the pure steel frame, the initial stiffness was 4.3 times higher, and the displacement ductility factors were 2.44-2.69 times higher. In the infilled prefabricated RACSWs, the wall cracks mainly extended along the connection between the embedded T-shape connectors and walls before finally connecting along the horizontal direction. Moreover, shear failure occurred in the specimens. The bearing capacity was 1.44 times higher than that of the pure steel frame, the initial stiffness was 2.8 times higher, and the displacement ductility factors were 3.32-3.40 times higher. The degradation coefficients of the bearing capacity were more than 0.85, indicating that the specimens demonstrated a high safety reserve.  
steel frame; infilled shear walls; recycled aggregate concrete; semi-rigid connection; seismic behavior +
Footnote †: journal: Applied Sciences
# A Task-Centric Cooperative Sensing Scheme for Mobile Crowdsourcing Systems
## 5 Performance Evaluation
### Determining Number of Active Participants  
In IPSA, the number of active participants/grids is determined according to the loss threshold. The goal is to use optimal number of active participants under the constraint that the approximation error (of the training data set) is kept below the threshold. In Figure 4, we plot the participant numbers _versus_ the loss threshold (over the range of 0 and 1) running the IPSA algorithm.  
As expected, along with the increase of loss threshold, the required optimal number of active participants decreases. More specifically, when the loss threshold exceeds 20%, only one active participant is needed. This shows that the data same collected from the neighboring region is highly spatially-correlated. On the other hand, it is worth noting that only 24 participants will be sufficient to estimate the remaining idle participants' measurements within 1% error.  
As shown in Figure 5, the number of participated tasks for each participant for the whole task set over the 11 days' testing sensing period ranged from 3205 to 3685. Each participant performed an average of 3456 tasks. The standard deviation of the participated tasks for all participants is 136.7, which confirmed that the IPSA can maintain good fairness among participants.  
Figure 3: Simulation setup and environmental observations in Intel Berkeley Research lab. (**a**) Sensor deployment and grid-based task area; (**b**) Possible task areas and participant-moving areas.