esa-sceva/satcom-chunk-collection huggingface dataset:
# Parametric Analyses of the Response of Masonry Walls with Reinforced Plaster
###### Abstract
The high level of complexity associated with the modeling and analysis of masonry structures has led to the development of very different approaches, based on different hypotheses, with different levels of accuracy and computational costs. The possibilities span from distinct element models [20; 21; 22] to continuous finite element models, with the latter covering a variety of cases, depending on the level of discretization detail: homogeneous macromodels [23; 24; 25], simplified micromodels with no unit-joint interfaces [26], micromodels with simplified interfaces [27; 28], or detailed micromodels [29], with diverse options of material constitutive laws. For this research, the numerical procedure is based on finite element modeling, which considers the local mechanical parameters of bricks and mortar with a simplified micro-modeling approach. The preliminary experimental outcomes of the experimental program were used to validate the numerical models. The aim of the paper is to present and discuss the results of the parametric study, to allow a satisfying characterization of the retrofitting method and to guide researchers and engineers in their choice of proper design parameters. After the presentation of the main data about the experimental tests, the implemented numerical model is described, together with the constitutive models adopted for the different materials. The parametric analysis follows, with discussions on the effect of the analyzed parameters on the performance of the reinforced wall. The results of this study show that the connectors do not have a significant influence on the retrofitted wall capacity, but may prevent instability phenomena if a proper number is considered. Furthermore, the strength and thickness of the plaster may be beneficial in terms of collapse load, even if, regarding the thickness, it is always recommended to use smaller values to keep the seismic mass as low as possible. Lastly, the presence of the steel reinforcement is convenient to increase the maximum load and, especially, the ductility of the system. As an alternative to the steel mesh, the use of fiber-reinforced concrete may be considered, with analogous performance of the retrofitted wall, but with more limited damage.
## 3 Results
### Observed Defects and Non-Destructive Testing Results  
To verify the integrity of the welded joints, both non-destructive and destructive testing methods were applied in accordance with EN ISO 14555 and ISO 5817 standards. Non-destructive inspections included visual examination, liquid penetrant testing (PT), and radiographic testing (RT) where applicable.  
Visual inspection served as the initial quality check to identify surface-level discontinuities such as cracks, undercuts, incomplete fusion, or excessive spatter. As discussed in Section 3.2, no critical surface flaws were found that would compromise structural performance. Minor variations, such as \"hot welds\" in some ARC CF samples and \"cold welds\" in certain ARC SG welds, were observed but did not affect the mechanical integrity of the joints.  
Liquid penetrant testing was performed on representative samples to detect any surface-breaking cracks or porosity not visible to the naked eye. All tested welds passed the PT inspections without indications of unacceptable surface defects, confirming the effectiveness of the parameter control.  
Radiographic testing was carried out on selected samples to identify internal discontinuities such as slag inclusions, porosity clusters, or incomplete fusion within the weld metal. The radiographs confirmed that all welds met the acceptance levels defined in ISO 5817 for Quality Level C, which is suitable for general structural applications. Minor isolated pores were occasionally detected in ARC CF welds, likely due to ferrule residue or gas entrapment, but remained within acceptable limits.  
Figure 8: Vickers hardness (HV10) distribution across the weld metal and adjacent base material for macro-sections 3-3 (ARC CF), 6-3 (ARC SG), and 9-3 (ARC SRM) on S235 JR + N structural steel.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Area in the Welded Joint** & **Macro-Section 3-3** & **Macro-Section 6-3** & **Macro-Section 9-3** \\ \hline Base material S235 JR + N & 147.8 & 131.3 & 140.6 \\ Weld metal & 295.0 & 235.0 & 256.0 \\ Base material S235 JR + N & 131.2 & 155.2 & 170.3 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Average HV10 hardness values for samples 3-3, 6-3, and 9-3.  
In addition to non-destructive testing, bend tests at angles of 30\({}^{\circ}\) and 60\({}^{\circ}\) were conducted to evaluate the ductility and bond strength of the welded studs. All samples withstood the required bending without failure at the weld collar, verifying sound metal-lurgical fusion.  
Quality assurance in arc welding requires systematic control of imperfection levels, especially in production environments [32].  
Overall, the combined results from visual inspection, penetrant testing, radiographic testing, and mechanical bend testing demonstrate that the applied welding parameters and techniques produced reliable and defect-tolerant stud welds compliant with relevant standards.
# Camera-Based Continuous Heart and Respiration Rate Monitoring in the ICU
## 3 Results
### Heart Rate Extraction
#### 3.1.2 Error Analysis of NIR-Based Extraction  
Similar to the RGB recordings, the NIR recordings of patients with an irregular rhythm had inaccurate heart rate measurements; as such, the NIR recordings of the same group of 11 patients had low agreement with the reference. Another observation is that in the case of patient 20, the NIR-based heart rate became inaccurate because of a blinking light that illuminated the patient; the light blinked with a period of 5 s, which is outside of the heart rate range, but one of its higher-order harmonics did fall in the heart rate band at 48 BPM, which caused a peak in the Fourier spectrum which for many segments was higher than the heart rate peak. Furthermore, we also observed that for a large group of patients, the RGB recording never became dark enough to the point where the RGB-based measurement became inaccurate, which means that only a subset of the NIR recordings was relevant for heart rate monitoring. Also, the NIR camera was out of focus for the recording of patients 3 and 16; the resulting blurring of the images influenced the optical flow calculation. Because of this, the optical flow measurement becomes much less sensitive  
Figure 6: Examples of heart rate extraction errors due to limited face visibility. The detected face ROI is marked in red. The area around and including the patients face is blurred to ensure anonymity of the participants. (**a**) Overex from face detection failure and limited visibility. (**b**) Face detection failure due to limited face visibility. (**c**) Limited face visibility due to equipment and blanket.  
to smaller motion, which could harm the performance of the motion detection. Furthermore, the face detection was affected by this, resulting in a more poorly defined face ROI. Finally, patients 19 and 23 had a problem where the NIR light source was not correctly pointed at the patient, causing inaccurate extraction of the heart rate. In the end, the NIR recordings of seven patients (1, 7, 10, 15, 17, 29, and 34) remained where NIR measurements could feasibly contribute to the coverage of the monitoring system. In Figure 7, we see the overall agreement, MAE, and coverage of the heart rate extraction in these selected NIR recordings plotted against the NIR motion threshold. In the graph, 100% coverage indicates the whole recording. Since the NIR-based measurements were only evaluated where the RGB face ROI brightness was below 25, the coverage did not reach 100%.
# Design and Implementation of a 2D MIMO OCC System Based on Deep Learning
## 4 Experimental Setup
### Hardware Setup  
In this paper, a variety of distances and velocities were employed to evaluate the efficacy of our model. Our goal was to ensure that our model is suitable for use in highly mobile scenarios, such as vehicle-to-vehicle (V2V) systems that employ the OCC technique and can cover long distances. To accomplish this objective, we varied the speed with which we moved the camera receiver over a predetermined distance during each test. Then, we calculated the BER for each velocity value, allowing us to assess the system's reliability under conditions of high mobility. In addition, we assessed the efficacy of the system by evaluating the precision and execution time of the LED detection model for each frame of the deep learning algorithm.  
Figure 8 depicts the 8 \(\times\) 8 rectangle LED matrix on the transmitter side and the Brio UHD Pro Camera, manufactured by Logitech International S.A. in Lausanne, Switzerland, on the receiver side. In addition, our system contains sensors, such as the DHT22 sensor manufactured by ASAIR in Guangzhou, China for measuring temperature, humidity, and vibration and the TFmini S LiDAR sensor produced by Benewake in Beijing, China for measuring the distance between two objects in real-time; the output can change based on environmental conditions. In addition, our system can be applied to a variety of applications, such as the V2V system, where the transmitted data can include, among other vehicle-related information, speed and travel direction.  
The four corners of the LED that were most distant served as corner detection anchors. From these corner coordinates, it was straightforward to calculate the coordinates of all LEDs using a perspective transformation. The system transmitted data using 54 LEDs, including the SN bits.  
Figure 7: (**a**) Sample of raw data before labelling; (**b**) After labelling process.
## 4 Implementation of the Proposed UniChain System
### Adding a New Student Node  
The procedures of adding a new student node begins when a request is sent by a university node. The university node sends the Ethereum address of the new student node, its ID and the requested role to the NC for validation. Similar to the process of validating a request sent for adding a university node, voter nodes validate and authenticate that received request by ensuring and confirming that the received request is related to a legitimate student and the non-existence of a registered student matching that received ID. If the request is accepted and validated, the NC updates its local memory with the student's ID, its Ethereum address and a \"student\" role. The NC creates a new SRHC for the new student node whose address will be forwarded to the university. The new student's account information will be sent to the student node from the university node that forms the request.
## 3 Proposed Protocol Architecture  
### Protocol Architecture  
To introduce efficient addressing and routing scheme in our proposed global IP-WSN, we use sensor proxy mobile IPv6 (SPMIPv6) architecture [8]. The proposed architecture has different functional components. The architecture mainly consists of a sensor network-based localized mobility anchor (SLMA). Here we use SLMA as a border router (BR) since it provides an interface between the IP-WSN domain and the traditional Internet domain. It also contains a sensor network-based mobile access gateway (SMAG). For the end-to-end communications, it contains many full functional devices (FFDs) and reduced functional devices (RFDs). This architecture is designed in a hierarchical approach. We discuss the individual components of the proposed architecture in more detail in the following subsections with the help of Figure 1. Figure 1 consists of two IP-WSN domains which are connected to the Internet domain by a BR. The IP-WSN domains again consist of different SMAG domains. The SMAG domains further consists of FFDs and RFDs.
## 5 Conclusions  
In this study, we developed a framework to obtain enhanced composite images from otoscope video clips and evaluated its effectiveness with a reader study. The data used in our study were acquired from a hand-held HD video imaging system. The results of this study have shown that an appropriate frame selection applied on otoscopy videos can significantly improve the diagnostic quality of composite images generated by these selected frames. We envision that the proposed approach has the potential to provide valuable diagnostic information in the form of normative TM information.  
Our system could be very valuable to provide a comprehensive view of the eardrum to clinicians, supporting more appropriate treatment. These composite images may increase the accuracy and efficiency of automated image analysis systems. Such a system can analyze the composite image, on a cloud-based platform, produced from a video-otoscope taken by a trained healthcare individual. These systems could be particularly helpful in case of shortage of specialists who can accurately diagnose eardrum abnormalities, especially in low- and middle-income countries and/or in rural areas [9]. Moreover, this single image could be stored and transferred between health institutions or computation environments instead of a whole video sequence.  
More data is needed to explore the generalization of the proposed framework to different kinds of eardrum abnormalities. For example, some abnormalities (TM with retraction, perforation with discharge, and cholesteatoma) are more difficult to diagnose than some particular ones such as clear perforation [33]. A further research study will be needed to build models that could analyze the whole spectrum of otoscopic videos faced in clinical settings.  
Conceptualization, H.B., M.K.K.N., and M.N.G.; Data curation, A.C.M., G.E., J.S., C.E., T.T., and N.T.-S.; Formal analysis, L.Y.; Funding acquisition, A.C.M. and M.N.G.; Investigation, A.C.M. and M.N.G.; Methodology, H.B., M.K.K.N., and M.N.G.; Project administration, A.C.M. and M.N.G.; Software, H.B.; Supervision, A.C.M. and M.N.G.; Writing--original draft, H.B.; Writing--review and editing, H.B., A.C.M., M.K.K.N., G.E., J.S., C.E., T.T., N.T.-S., L.Y., and M.N.G. All authors have read and agreed to the published version of the manuscript.  
**Funding:** The project described was supported in part by Award R21 DC016972 (PIs: [PERSON], Moberly) from National Institute on Deafness and Other Communication Disorders. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institute on Deafness and Other Communication Disorders or the National Institutes of Health. The APC was funded by the institutional funds by Wake Forest School of Medicine.  
**Conflicts of Interest:** Authors [PERSON], [PERSON], and [PERSON] are shareholders in Otologic Technologies. Authors [PERSON] and [PERSON] are paid consultants and serve on the Board of Directors for Otologic Technologies.
## 2 Related Work
### Network Structure and Diffusion Dynamics  
Network topology significantly influences information diffusion patterns, a principle established by [PERSON] et al. (2003) [4] and empirically validated by [PERSON] et al. (2007) [5] through their analysis of cascading behavior. Building on these foundations, [PERSON] and [PERSON] (2021) [16] in their recent work developed a distance-aware optimization model to identify influential nodes. Their approach implicitly maximized local coverage while minimizing global overlap under the Independent Cascade diffusion model. [PERSON] et al.  
(2019) [17] mapped networks into three-dimensional space (behavior influence, attribute influence, and topological influence) to analyze how each dimension affects diffusion.  
[PERSON] et al. (2023) [18] introduced fuzzy sign-aware diffusion models that considered both trust and distrust relationships. Their approach defined new rules to determine a user's state based on information received from active neighbors, showing improved prediction accuracy in Bitcoin networks.  
Research on Reddit networks specifically has revealed important insights. [PERSON] et al. (2015) [19] examined information lifespan and propagation across Reddit, finding that content is enhanced or weakened according to the topic and the network's dynamic nature. [PERSON] et al. (2020) [20] evaluated document clustering and topic modeling techniques on Reddit, demonstrating that neural embedding representations delivered the best performance. [PERSON] et al. (2024) [21] studied the impact of Reddit posts on retail trading behavior, finding that social media posts had significantly better effects than traditional news articles. [PERSON] et al. (2024) [22] further extended temporal considerations by investigating time-varying attractiveness in competitive information diffusion. Their Markov multi-information diffusion model incorporated three critical parameters: attractiveness degree, information boom time, and information prosperity index.  
The complexity of modern social networks has prompted research into multiplex network structures. [PERSON] et al. (2025) [23] developed a framework utilizing node dynamics time-series data for network alignment in multiplex social networks. Their approach employed diffusion models to simulate information propagation across multiple platforms, achieving a high accuracy in identifying both interlayer and intralayer links. Similarly, [PERSON] et al. (2022) [24] proposed a fuzzy-based link prediction algorithm in multiplex networks using an information diffusion perspective, demonstrating improved accuracy compared to traditional methods. These approaches highlight the growing importance of considering multiple relationship types and interaction channels in diffusion modeling.
## 5 Discussion
### What Have Been the Targeted Application Domains?  
Figure 11 demonstrates the application sectors of BN and GT models for jointly monitoring safety and security events. The key sectors are the maritime (14%), vehicle transportation (13%), railway (13%), nuclear (6%), chemical (6%), gas and pipelines (5%), smart grid (5%), network security (5%), air transportation (3%), public sector (3%), and CPS (3%) industries. The other preferred application sectors were software (2%), water traffic system (2%), ICS (2%), education (2%), UAV (2%), complex systems (2%), oil what handling (2%), process plant (2%), socio-technical systems (2%), SoS (2%), navigation environment (2%), petroleum plants (2%), mobile networks (2%), cognitive radio networks (2%), Asian games (2%), and medical (2%).
## 2 Design of the Proposed DPA
### Design of the Dynamic Power Dividing Network
The AM-AM characteristics are maintained at an acceptable level since it has uneven power drive by about 1 dB to the peaking amplifier at the peak power level. Figure 3 presents the simulated overall gain and PAE  
Figure 1: Proposed dynamic power dividing network: (**a**) Schematic; (**b**) Layout. case 2 compared to the conventional DPA. Both cases were designed with an identical load network and a transistor configuration for the same saturated output power. Compared to the conventional case, case 2 exhibited 3.6 dB higher power gain, 4.1%p higher PAE at an OBO of 6 dB, and 3.3%p higher PAE at the peak power while maintaining good AM-AM characteristics. Figure 4 shows the design parameters of the interstage transformer used for the dynamic power dividing network. The interstage transformer was designed with a single primary turn with two secondary turns, which can feed the carrier and peaking amplifiers. Therefore, three coupling coefficients are formed between the separate turns, along with three values of self inductances. Altering the coupling coefficients of the interstage matching network would provide different power dividing ratios to achieve the various cases shown in Figure 2. For Case 2, the coupling coefficient between the driver-carrier and driver-peaking paths k21 and k31 had nearly equal values, with a little uneven drive to the carrier path as shown in Figure 4b. This set of values slightly boosts the power gain of the DPA low power operation and can still maintain favorable AM-AM when \(Z_{P\_in}\) in Figure 1 is appropriately matched to \(Z_{P\_TF}\) through the T-section matching network, using C\({}_{P2}\)-C\({}_{P1}\)-L\({}_{P1}\). The \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Case** & **Bias** & **Efficiency** & **Dynamic Range** \\ \hline Case 1 & Class-B & 25\% & 3.5 dB \\ Case 2 & Class-B & 25\% & 3.5 dB \\ Case 3 & Class-AB & 22\% & 1.0 dB \\ Case 4 & Class-AB & 20\% & 0.3 dB \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Case comparison of the dynamic power dividing network. Figure 2: Three cases of the dynamic power dividing network compared to the conventional power divider: (**a**) Power dividing ratio; (**b**) Overall PAE; (**c**) AM-AM characteristics. T-section matching network L\({}_{\text{C2}}\)-C\({}_{\text{C1}}\)-L\({}_{\text{C1}}\) of Figure 1 is also optimized to provide the DPA with the required phase offset and proper impedance matching for \(Z_{\text{C}\_in}\). Figure 4: Design parameters of the interstage transformer: (**a**) Self inductance of the driver primary turn, carrier path secondary turn, and peaking path secondary turn; (**b**) Coupling coefficients. Figure 3: Large signal performance comparison with and without the dynamic power dividing network.
## 4 Discussion and Validation
The following graph represents the impact of the turbulence duration time on an aircraft's vertical displacement as a function of different normal acceleration variations, according to the proposed methodology [Equation (28)]. Observing Table 12 and Figure 11, it can be concluded that the exposure time to turbulence is also a very influential factor in the vertical displacement of an aircraft. For example, comparing Samples \"5\" and \"7\" [Table 12], which have the same normal acceleration variation of \(-\)1.2 g, with a difference of one tenth of a second, Sample \"7\" presents four meters more in the aircraft height loss. Likewise, Figure 11 shows that as the normal acceleration variation increases, the influence of time on the aircraft's vertical displacement becomes greater, following an exponential function. Thus, a turbulence of 1.8 s duration with a normal acceleration variation of 1 g presents a difference of 10 m compared to one of 2 s, while a turbulence of 3 g variation differs by more than 20 m.  
According to the values of time and normal acceleration provided by NASA [Table 12], which were measured during in-flight turbulence events, it is observed that for larger values of normal acceleration variations, the duration times of turbulence gusts are shorter, resulting in more abrupt altitude losses but with the same order of magnitude. Thus, the estimated maximum altitude losses remain around 30 m, as shown in Table 12, since these values are consistent with those obtained in this paper and also with those provided by Airbus and experts. Finally, it should be noted that the obtained additional clearance values for mountainous terrains prove the need to apply an additional margin of 100% of the MOC on certain occasions, especially in phases of flight at high altitudes where wind speeds are stronger, whereas in phases of flight within the Terminal Control Area, which are at lower altitudes, the application of a 100% margin would penalize the procedures and could even make their design unfeasible when a lower clearance also guarantees their safety. Figure 11: Vertical displacement as a function of normal acceleration variations and time.