esa-sceva/satcom-chunk-collection huggingface dataset:
# Response of Seismically Damaged Steel Reduced Beam Section Joints under Fire
## 2 The Reference Archetype and the Considered Scenarios
### Details of the Designed Beam-to-Column Joints  
Figure 2a shows the details of the designed beam-to-column joints that were sub-structured from the reference building. The external joint of the MRF consists of a welded RBS connection on one side of the column (i.e., the connection of the MRF) and a flush end-plate bolted connection on the other side (i.e., the connection of the gravity load-resisting bay). The internal joint comprises two welded RBS connections per column side.  
All beam-to-column connections were designed according to EN1993-1-8 [26], while the details of reduced beam sections were designed according to EN1998-3 [25]. In addition, RBSs were alternatively designed to provide two different levels of flexural resistance: (i) RBS with flexural resistance (M\({}_{\text{RBS,Rd}}\)) equal to 80% of the beam plastic moment (M\({}_{\text{pl,Rd}}\)) (hereinafter referred to as RBS-80) and (ii) RBS with M\({}_{\text{RBS,Rd}}\) equal to of 0.65 M\({}_{\text{pl,Rd}}\) (hereinafter referred to as RBS-65).  
The geometrical features of both the external and internal investigated RBS (i.e., a, b, c, and R) are depicted in Figure 2b and summarized in Table 2.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Storey**} & \multicolumn{2}{c}{**Gravity Load Frame**} & \multicolumn{2}{c}{**External MRF Bays**} & \multicolumn{2}{c}{**Internal MRF Bay**} \\  & **(Outer Bays)** & & & & & \\  & **Column** & **Beam** & **Column** & **Beam** & **Column** & **Beam** \\ \hline I & HEB300 & IPE400 & HEB 500 & IPE500 & HEB550 & IPE500 \\ II & HEB300 & IPE400 & HEB 500 & IPE500 & HEB550 & IPE500 \\ III & HEB300 & IPE400 & HEB 500 & IPE500 & HEB550 & IPE500 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Cross section of the investigated moment-resisting frame.  
Figure 1: Moment-resisting frame (MRF) and concentrically braced frame (CBF) layout and main geometrical features of the building archetype (**a**) and Perimetral MRF (**b**) view.  
The resistance of the rib-stiffened connection and the column web panel of the RBS joints were designed to resist the effects due to Equations (1) and (2), respectively.  
\[\mathrm{M_{c,Ed}}=\mathrm{M_{RBS,Rd}}+\mathrm{V_{RBS}}\cdot\mathrm{e} \tag{1}\]  
\[\mathrm{M_{cc,Ed}}=\mathrm{M_{RBS,Rd}}+\mathrm{V_{RBS}}\cdot\left(\mathrm{e}+ \frac{\mathrm{d_{c}}}{2}\right) \tag{2}\]  
where \(\mathrm{M_{c,Ed}}\) and \(\mathrm{M_{cc,Ed}}\) are the design bending moment at the column face and at the column axis, \(\mathrm{V_{RBS}}\) is the shear force in the reduced beam section, \(e\) is the distance between the beam plastic hinge and the column face, and \(\mathrm{d_{c}}\) is the column depth.  
In order to enhance the resistance of the welded connection and to prevent the yielding of the beam segment close to the column, double rib stiffeners were designed according to [15,16].
# A Workflow for Creating Gastric Computational Models from SPARC Scaffolds
###### Abstract  
In-silico studies are an ideal medium to model and improve our understanding of the mechanisms underlying gastric motility in health and disease. In this study, a workflow to create computational models of the stomach was developed using SPARC scaffolds. Three anatomically based finite element method (FEM) models of the rat stomach incorporating experimental measurements of muscle layer thickness and fiber orientations across the stomach were developed: (i) 2D (surface) FEM model with no thickness, (ii) 3D (volume) FEM model with a fixed thickness across the longitudinal and circular muscle layers, and (iii) 3D (volume) FEM model with varying thickness across the longitudinal and circular muscle layers. The three FEM models were subsequently used in whole-organ slow wave simulations and the impact of anatomical details on the simulation outcomes was investigated. The 3D FEM model with varying thickness was the most computationally expensive, while the 2D FEM model provided the fastest solution (a 200 s simulation took 8 min vs. 38 h to solve). The spatiotemporal profiles of the slow wave activation and propagation in the three FEM models were in good agreement. The largest temporal difference of 1 s in cellular activation was observed between the 2D FEM model and the varying thickness 3D FEM model in the most distal-stomach regions. These FEM models and developed workflow will be used in in-silico studies to improve our understanding of the structure-function relationship in the stomach and identify the optimal parameters of electrical therapies, an alternative treatment for the motility disorders in the stomach. In addition, the developed workflow can be readily used to generate computational models of other organs using SPARC scaffolds.  
gastric computational models; SPARC scaffolds; gastric slow waves; rat stomach; finite element method +
Footnote †: journal: Applied sciences
# Performance Optimization of MANET Networks through Routing Protocol Analysis
## 2 Materials and Methods
### Simulation Design  
The case selection in the present research was significant because it would affect the design of the simulation scenarios. MANET can be used in real applications on mobile communication devices such as notebooks, smartphones, and data exchange sensors. In this study, the chosen case involved a smartphone or sensor attached to a vehicle; therefore, it could send packet data to other devices or base stations. To support this scenario, we used a simulation area of 1000 \(\times\) 1000 m, the UDP transport protocol, and a packet size of 512 bytes with delivery every 10 times in 1 min with a maximum number of packets of 1000 on each connection. Table 1 shows the parameters and values used during the research.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Simulation time & 200 s \\ Area of simulation & 1000 \(\times\) 1000 m \\ Transportation protocol & UDP \\ Packet type & CBR \\ Packet size & 512 bytes \\ Rate of packets & 10 packets/second \\ Maximum packets & 1000 packets \\ Propagation model & Two Ray Ground \\ Initial energy & 1000 J \\ TxPower & 1.3 W \\ RxPower & 1.4 W \\ Idle power & 0.7 W \\ Sleep power & 0.01 W \\ RREQ\_RETRIES AODV & 2, 3 (default), 4 \\ MAX\_REEQ\_TIMEOUT AODV & 5, 6, 7, 8, 9, 10 (default), 11, 12 s \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation parameter.  
Figure 1: Research steps.
## 2 Materials and Methods  
### Materials and Chemicals  
A commercial probiotictic that was approved by the Ministry of Food and Drug Safety of Korean government was purchased from Seoyun family Co., Ltd. (Seoul, Republic of Korea) and was used as a positive control in this study. Thiobarbituric acid (TBA), malondialdehydedehyde (MDA), hydroxylamine hydrochloride, 5,5'-dithiobis (2-nitrobenzoic acid) (DTNB), 2-4-dinitrophenylhydrazine (DNPH), guanidine hydrochloride, acetic acid (AA), n-butyric acid (BA), propionic acid (PA), and anhydrous methanol were purchased from Sigma-Aldrich Chemical (St. Louis, MO, USA). Dextran sodium sulfate (DSS) was purchased from MP Biomedicals (Santa Anna, CA, USA). The antibody for interleukin 1 beta (IL-1\(\beta\)) was purchased from Abcam (Waltham, CA, USA). Antibodies for nuclear factor Kappa-B (NF-\(\kappa\)B) p65, interleukin-6 (IL-6), tumor necrosis factor-alpha (TNF-\(\alpha\)), \(\beta\)-actin, GAPDH and DAPI solution (1 mg/mL) were procured from Themo Fisher Scientific Inc. (Waltham, MA, USA). The antibody for horseradish peroxide (HRP)-conjugated goat anti-rabbit was obtained from Jackson ImmunoResearch Inc. (West Grove, PA, USA). Formic acid was obtained from Daejung chemicals (Siheung-si, Republic of Korea). Tri-chloroacetic acid (TCA) was purchased from Wako Pure Chemical, Inc. (Kyoto, Japan).
# Key Roles of Plasmonics in Wireless THz Nanocommunications--A Survey
## 2 WiNoCs
### Hybrid Optical Wireless NoCs  
On the other hand, optical technology when combined with chip scale wireless interconnections may be as well considered as a promising hybrid NoC solution to overcome the performance bottlenecks of the current state of the art NoC architectures. Unfortunately, all plasmonic based solutions proposed in the literature for wireless applications do not overcome the problem of integration with SOI based NoC platforms. Moreover, plasmonic waveguides display high propagation losses and, therefore, they are not suitable for implementing long range on chip interconnections. An appropriate solution would be based on the adoption of the hybrid combination of plasmonic resonators as nanoantennas, while keeping dielectric waveguides as the feeding elements [6]. Hence, the employment of plasmonic nanoantennas adjusted to dielectric waveguides for building nano-optical wireless links instead of conventional plasmonic waveguide links, with short range propagation limitations would be a promising solution.  
Another key design issue for building successfully such hybrid NoC architectures, is the implementation of the perfect coupling of plasmonic antennas with conventional silicon waveguides, guaranteeing full compatibility with Si photonic and nanophotonics circuitry standards. Waveguidecoupled plasmonic antennas may become a drastic solution for a successful coupling without losses [40; 41], enabling a hybrid optical wireless approach in the NoC design. The efficient coupling between plasmonic antennas and SOI waveguides is a non-trivial issue, as an on-chip, point-to-point connection normally requires matched directive nanoantennas. The nanoantenna shape and size should be properly designed so as to ensure impedance matching to the waveguide, and directional emission in the desired direction [42].  
Moreover, hybrid wireless optical on chip communication takes advantage of the entire WDM spectrum when propagating in the optical wired links, guaranteeing even higher multiple capacities, as required by intra-chip communications [43]. Various configurations of plasmonic nanoantennas for supporting wireless-optical on chip communication have been proposed in the literature, such as plasmonic horn nanoantennas [44], a directional plasmonic Yagi-Uda nanoantenna placed on a dielectric waveguide [45], or a plasmonic nanoantenna array on a dielectric waveguide [46], or various configurations of plasmonic Vivaldi antennas (double, or an array of them) to name but a few [47; 48]. Plasmonic antennas will be described more analytically in the upcoming section.
# Somersault Foraging and Elite Opposition-Based Learning Dung Beetle Optimization Algorithm
## 5 Application of SFEDBO Algorithm
### Three-Dimensional Unmanned Aerial Vehicle Path Planning
#### 5.3.3 Simulation Experiments and Analysis  
The simulation experiment data of the five algorithms are shown in Table 7, and the experimental results are shown in Figures 3-5. In Table 7, the first column is the algorithm name, the second column is the flight path length, the third column is the flight altitude variation, the fourth column is the deflection angle variation, and the fifth column is the optimal fitness value. The flight path length and flight altitude variation of the path planned by the SFEDBO algorithm are the best among the five algorithms, indicating that the path planned by it not only satisfies the shortest distance but also ensures stable flight altitude. However, its flight deflection angle is slightly inferior to the DBO algorithm. But, from a comprehensive perspective of these three aspects, the path planned by the SFEDBO algorithm is still the best among the five algorithms.  
Figure 3: Three-dimensional path-planning diagram.  
Figure 2: Three-dimensional path-planning space.  
Figure 4: Top view of path planning.  
Figure 5: Convergence diagram.  
As evident from Figure 3, the paths planned by the three enhanced algorithms exhibit significant improvement over those of the DBO algorithm and the GWO algorithm. From the top view of Figure 4, the SFEDBO algorithm and the MSADBO algorithm have found a flight route closer to the destination through the gaps between the four impassable areas, while the DBO algorithm and the GWO algorithm have only found a path around the sides. Therefore, it can be concluded that with the enhancement of the tumbling foraging strategy and the adaptive strategy, the SFEDBO algorithm can accurately find an optimal path. Figure 5 displays the convergence curves of the five algorithms. As evident from the figure, the incorporation of the EOBL strategy to enhance the diversity of the initial population results in the fitness of the path planned by the SFEDBO algorithm surpassing the other three algorithms in the early stages of iteration. Only the SGMDBO algorithm manages to keep pace with the SFEDBO algorithm during this initial phase. However, upon examining the final iteration curve, it becomes evident that the fitness of the SFEDBO algorithm surpasses that of the SGMDBO algorithm. Conversely, both the GWO and DBO algorithms exhibit not only a sluggish convergence speed during the planning process but also a tendency to converge towards local optima in the later stages of iteration.  
In summary, considering indicators such as flight path length, flight altitude variation, and flight deflection angle, the path planned by the SFEDBO is better. Therefore, the SFEDBO algorithm has the ability to handle such problems and has advantages over other algorithms.
## 4 Discussion
The inclusion of both plaster and digital models for comparison enhances the reliability of the findings. Additionally, the study demonstrated intra- and inter-examiner reliability, reinforcing the robustness of digital measurement techniques. The use of the latest-generation intraoral scanner, CEREC Primescan, ensures that the results reflect the current advancements in digital dentistry, making them relevant for contemporary clinical applications. The study had some limitations. The study was conducted on a relatively small and homogeneous sample, primarily consisting of young adults with fully erupted permanent dentition. This limits the generalizability of the findings to populations with mixed dentition or those with dental anomalies. The accuracy of intraoral scanning may be affected by factors such as patient cooperation, intraoral lighting conditions, operator expertise, saliva, and soft-tissue dynamics. Posterior teeth and areas with metal restorations may introduce distortions due to scanning challenges. Lastly, while measurements were performed by experienced examiners, the potential for inter- and intra-examiner variability remains a consideration. Future studies should compare different intraoral scanner systems across various clinical scenarios, as this would also be valuable. Lastly, the integration of intraoral scanning with emerging technologies such as augmented reality (AR) and virtual treatment planning could enhance its role in digital dentistry, further improving treatment outcomes and patient experiences.
# Forward/Backward Decomposition for Dispersive Wave Propagation Measurements
## 4 Experimental Validation
### Experimental setup
In the experiments presented here, the sampling time was arbitrarily selected to be 10 times the duration of the excitation pulse, and the response was not windowed or manipulated in any way. In order to minimize the effects of random noise, measurements were time-averaged by sending pulses several times and averaging the resulting responses. This assumes the response is repeatable, which is reasonable in this case since the excitation is identical and synced for each run. The laser vibrometer measurements were averaged 3 times for a \(\sqrt{3}\) factor improvement in the signal-to-noise ratio.
# Fog-Based Two-Phase Event Monitoring and Data Gathering in Vehicular Sensor Networks
## 4 TPEG Framework  
### Overview  
Figure 2 depicts the overall phases of the TPEG scheme. There are five steps as follows:  
1. Data monitoring: Nodes work in LCS mode, and they sense the environment and generate the data. The data have a relatively low generation rate and confidence and are uploaded to the RSU through V2I or V2V communications.
2. Event checking: RSU checks the confidence/probability of an event, and it initiates an event-checking procedure when the event probability is high. When the confidence is low, nodes just keep silent, and no event-checking procedures are needed.
3. Deep sensing: Some nodes are selected to transfer to \"deep sensing\", where they work in HCS mode and sense more accurate data about the environment.
4. Data upload: The data generated in HCS mode are uploaded to the RSU for final event verification. Data could be uploaded directly, forwarded to neighboring nodes or wait until encountering a new RSU. Nodes would adaptively decide their strategy for data upload based on whether they are within the coverage of an RSU or encountered nodes.
5. Event decision: The ITS cloud process the gathered data and make a decision about the events. Some data are archived on the cloud, and some feedback is sent back to the RSUs.  
The monitoring procedure in Step 1 runs as a background process with low cost, while still being able to alert the possible events. TPEG would upload the data of the event at the deep sensing step, while suppressing the message communications at the monitoring step. It is worth being noted that as nodes are moving along the road in VANETs, TPEG is a distributed framework where the monitoring and deep sensing procedures might execute at different vehicular nodes or at different RSUs.  
In the following subsections, we present the detailed description of the main steps of the scheme and algorithm.
# Multi-Source Data Fusion-Based Grid-Level Load Forecasting
## 2 Materials and Methods
### Implementation Framework for Multi-Regional Load Forecasting Integration
#### 2.5.4 Multi-Regional Forecast Fusion  
The core innovation of our framework lies in the weighted fusion methodology for integrating provincial forecasts. Provincial forecasts are collected through standardized interfaces, with automated validation to ensure consistency. We calculate the three primary evaluation indices for each province and timestamp, and determine optimal provincial weights for each forecasting period using the PCA-based approach.  
For each time point t, we calculate a dynamic adjustment coefficient \(\alpha_{t}\):  
\[\alpha_{t}=\beta\sum_{i=1}^{n}(accuracy_{t,t}\cdot w_{i,t}) \tag{20}\]  
The final integrated forecast for time t is calculated as  
\[Load_{final}=n_{provinc}\cdot\sum_{i=1}^{n}\frac{\alpha\cdot w_{i,t}\cdot Load _{provinc,i,t}+(1-\alpha)\cdot(1-w_{i,t})\cdot Load_{main,i,t}}{\alpha\cdot w _{i,t}+(1-\alpha)\cdot(1-w_{i,t})} \tag{21}\]  
Our framework is implemented as an end-to-end pipeline that operates in both day-ahead and real-time forecasting modes, ensuring that the grid dispatch center benefits from both the specialized knowledge embedded in provincial forecasting systems and the systematic integration methodology.
# Retransmission Avoidance for Reliable Data Delivery in Underwater WSNs
## 5 Simulation Results and Performance Evaluation
### Performance Metrics  
We are defining few terms here based upon them we evaluate proposed and existing schemes.  
_Active Nodes_ are the number of sensor nodes which are taking part in data transmission and reception.  
_Delay_ is the time taken by a data packet from the source to the destination. It includes transmission delay, propagation delay, holding time and processing delay. It is measured in second(s). There are multiple surface gateways and more than one copies of the same data packet are relayed, hence a data packet corresponds to more than one delays. However, we consider the highest delay among them.  
_Energy Consumption_ is the amount of energy consumed from start to the end of the network lifetime. Total energy consumption comes from the accumulation of energy consumed by individual nodes during the data transmission phase.  
_Required Packet Error Rate \((Pr_{req})\)_ is the maximum number of erroneous bits allowed in the data packet to be accepted at sink. If erroneous bits in data packet exceeds \(Pr_{req}\) limit, surface gateway drops it.  
_PRR_ is the ratio of successfully received data packets at surface gateways to the data packets sent by source nodes.
# Smart Environmental Data Infrastructures: Bridging the Gap between Earth Sciences and Citizens
## 6 Wrap-up Discussion on New Research Challenges
#### 2.2.2 Challenge 3: Environmental Observation and Modeling Ontology  
A generic top-level ontology that may be used as a global model for integrated knowledge representation in environmental applications has to be designed. The ontology should enable the representation of dataset level geospatial metadata, but also record level environmental metadata, which includes specific application and sampling feature metadata, provenance metadata related to either observation processes or environmental models and data quality metadata. Furthermore, the ontology should support the representation of simple observation and model results, but also of complex multidimensional coverages, including possibly spatial and temporal dimensions. Dataset level metadata is supported by both ISO 19115 and GeoDCAT-AP. ISO 19115 provides also means for the modeling of geospatial data quality. Available observation data models [11; 33; 35; 56; 13] provide constructs for the representation of observation processes and features of interest, including sampling features. Complex coverage results and data quality metadata are also supported. The main limitation is that the generic properties supported for processes and application features do not enable still the automatic generation of efficient data and metadata storage schemata, and therefore, existing generic data storage implementations lack the required data access performance. Similar characteristics are also present in the SSN ontology [13; 44]. The combination of the record level sensor metadata representation of SSN with the GeoDCAT-AP dataset level metadata would be an interesting starting point. Such a combined ontology should be extended with necessary classes and properties to enable the representation of environmental model metadata and to support the automatic generation of efficient data storage structures. Those structures should enable the efficient integrated storage (in terms of space) and the efficient access (in terms of response time) to vector and raster observation and modeling results with all the required dataset level and record level metadata. Finally, the semantic integration of already existing vocabularies such as SWEET [14], CF, GEMET, and other [46; 47; 48] should be enabled by the system, to foster semantic interoperability between data sources and applications.