esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
In this paper, a physics-informed DeepONet based method for reconstructing structural displacement from measured strain is proposed. The method demonstrates excellent performance in displacement reconstruction for both Euler-Bernoulli beams and Kirchhoff plates with various restrictions. The following conclusions can be drawn:  
1. Using each equation in the GDEs of the beam or plate as the loss term, PI_DeepONet can converge to the solution operator of GDEs. The trained PI_DeepONet demonstrates the ability to accurately map the strain function to the displacement function under diverse loading conditions.
2. With the guidance of GDEs, the PI_DeepONet does not require paired input-output observations for the training process. The training datasets of the PI_DeepONet are constructed using the random strain function modeled by mean-zero Gaussian random fields (GRF), which eliminates the necessity of expensive simulations or costly physical experiments.
3. The imbalance between the back-propagated gradients of loss terms can be mitigated by adaptively updating the weight of each loss term. For the GDEs with more than one Neumann boundary condition, mitigating the imbalance helps the PI_DeepONet converge correctly and achieve an improved fitting accuracy for displacement reconstruction.  
Conceptualization, Z.Z. and X.W.; methodology, Z.Z., D.D. and Q.W.; software, Z.Z., Z.H. and K.X.; validation, X.Y., D.D., Q.W. and F.Z.; formal analysis, D.D., Q.W. and F.Z.; investigation, Z.Z., Z.H. and K.X.; resources, X.Y., Q.W. and X.W.; data curation, Z.Z. and F.Z.; writing--original draft preparation, Z.Z., X.Y. and X.W.; writing--review and editing, Z.Z., D.D., Q.W. and F.Z.; visualization, Z.H. and K.X.; supervision, X.W.; project administration, X.W.; All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflicts of interest.
## 1 Introduction
### Objective, Approach, and Paper Structure  
One piece of the puzzle of achieving traceable radiometric SAR system calibration is the derivation of the calibration factor from external calibration acquisitions. We propose for the first time to apply Bayesian statistical data analysis and hierarchical modeling to estimate parameters like the absolute calibration factor _including uncertainties and confidence intervals_ from SAR images. The proposed method is also applicable for similar data analysis tasks in radiometric calibration.  
Bayesian data analysis has been proven advantageous in many parameter estimation problems [12,13], but to the best of our knowledge so far it has not yet been applied for absolute radiometric SAR calibration. Bayesian analysis lends itself well to hierarchical data modeling [14], and is therefore a very good fit for the estimation problems that one faces during an external SAR calibration campaign. A general but short review of the methodology and an outline of its application in the domain of absolute radiometric calibration is given in Section 2.  
The suitability of the proposed method is shown through a case study in Sections 3 (campaign setup) and 4 (data analysis). The objective of the case study is conceptually identical to the derivation of the absolute calibration factor, although here the reflectivity of a point target shall be accurately determined. The presented case study is based on an external calibration campaign which was executed in April 2013. 15 corner reflectors were deployed as reference targets, and the reflectivity of DLR's new C-band Kalibri transponder prototype was derived from a series of eight data acquisitions from the Canadian RADARSAT-2 SAR system.  
Finally, the proposed approach is further discussed in Section 5 and conclusions are given in Section 6.
# Centralized vs. Decentralized Cloud Computing in Healthcare
## 7 Results
### RQ6: What Are the Research Gaps within Decentralized Cloud Healthcare Solutions?  
This section overviews the existing research gaps of decentralized cloud healthcare solutions that cause a number of unavoidable issues to make sure that such solutions will be able to scale, be interoperable, maintain privacy and security, use energy efficiently, and be integrated with real-world applications.  
The first of these voids relates to the effectiveness of distributed data storage and processing tools, load-sharing methods, and resource control solutions that will be adequate for accommodating large datasets and rolling user intensities without compromising response times.  
The second gap refers to the lack of interoperability between cloud healthcare systems and platforms that are decentralized and that may widely differ and use different protocols and systems. For the sharing of data and information, standardized protocols and data formats are needed, as are necessary networks, to achieve the promotion of safe and effective message transmission and data exchange between decentralized networks, while also ensuring that there is privacy of data and also integrity of the inputs fed to the system.  
The third gap relates to the more significant issue of patient privacy in decentralized cloud healthcare solutions, and consequently, there is a need to work on privacy-enhancing technologies like secure multiparty computation, zero-knowledge proofs, and differential privacy. These should help to control the processing of patient data in a cooperative way without infringing the privacy of the patients and should also be efficient for a distributed system of different nodes. Fourth, basic problems such as trust and security of distributed systems need to be resolved using identity management, access control, data origin authentication, and audits of the distributed network. The positive impact of smart contract technologies is that they enhance the decentralized healthcare system's dependability.  
Other dimensions that should be discussed in the context of decentralized cloud healthcare solutions are energy efficiency and sustainability; consequently, future studies should investigate energy-conscious algorithms, resource provisioning and management, and distributed energy management mechanisms that minimize the energy consumption and, thereby, the lifecycle costs of decentralized healthcare solutions.  
Also, additional extensive empirical real-world research and pilot implementations are needed to assess decentralized cloud healthcare solutions' feasibility, efficiency, and ergonomic properties. Future works include both large-scale experiments and system performance evaluations in various healthcare settings as well as more elaborate user feedback to support the confirmation of decentralization's feasibility and advantages.
# Effective IoT Congestion Control Algorithm
## 4 Results and Discussion
### Second Experiment: Proposed Approach with Onother Protocols I  
The topology used in this experiment is shown in Figure 2. The intra-protocol test was applied using three sending nodes. The installed internet stack protocol had the same protocol as the suggested algorithm. The fourth node was one of the algorithms outlined in Table 1 with a parameter setting as highlighted in Table 2. Each Si-Di represented the dedicated flow: S1-D1 represents the first flow, S2-D2 represents the second flow, and so on.  
The bar charts in Figure 4 show the average throughput when the bottleneck link speed was set to 1 Mbps and 2 Mbps. Figure 4a shows that TCP NewReno flows for a 1 Mbps bottleneck link occupied only 16.5% of the link. Furthermore, the 2 Mbps bottleneck link only occupied 17.9% of the link. Jain's Fairness Index will be discussed below. Figure 4b-d show the throughput of the TCP variant for TCP BBR, TCP Illinois and TCP Yeah, respectively. The throughput of these flows was compared to TCP Modified throughput. For all the tests, the throughput of the suggested protocol was higher when acceptable fairness was maintained.  
Figure 4: (**a**) Throughput versus TCP Variant (3 TCP Modified and 1 TCP NewReno); (**b**) Throughput versus TCP Variant (3 TCP Modified and 1 TCP BBR); (**c**) Throughput versus TCP Variant (3 TCP Modified and 1 TCP Illinois); (**d**) Throughput versus TCP Variant (3 TCP Modified and 1 TCP Yeah).  
Figure 3: CWND of the proposed algorithm versus time.  
The other evaluation metric is the fairness index. [PERSON]'s Fairness method is the optimal method to calculate this index. Equation (3) shows this equation. Figure 5 shows the fairness index when there are three flows of the suggested algorithm with a single flow of the standard algorithm for both bottleneck link speeds.  
[PERSON]'s Fairness Index = (\(\sum\)throughput(i)) ~2/(N \(\times\)\(\sum\) (throughput(i)) ~2)  
where N represents the link number and throughput(i) is the ith node throughput.
# Ad Hoc Data Foraging in a Life Sciences Community Ecosystem Using SoDa +
## 3 SoDa Architecture
### Workflow Meta-Language
#### 3.6.2 Resource Descriptions  
In contrast to the process description, a resource description captures _which_ computational tools are appropriate for a resource to function accurately, and how to determine the reliability of the resource so that its candidacy as a trusted information source can be reasoned. Thus, the intended usage of the resource description is to help automatically find, assemble, and construct executable workflows that are semantically the most likely implementation of a scientific inquiry. It does so by identifying the needed resources and providing tools to seamlessly collect the required data and query them in a pipeline. Recall that most of the resource descriptions in SoDa are contributed by a wide range of community members, some with limited domain expertise or credibility as curators.  
For example, FAIRBridge constructs the following resource description for the DisGeNET site automatically from the corresponding process description.  
% resource identifier  
create resource DisGeNETb (  
% resource narrative for machine consumption  
narrative _\"This browser access accepts an Entrez_  
_gene ID and returns its disease association\"_,  
% process contributors  
contributors _[[PERSON], [PERSON]]_,  
% applicable data integration tools  
meta:  
matcher _[Cupid, OntoMatch]_,  
wrapper _[FastWrap]_,  
mapping _[Determination Process: Semantic Type]_,  
% process testers and validators  
validators _[[PERSON], [PERSON]]_,  
);  
Resource descriptions such as DisGeNETb provide higher level details of process descriptions. Resource descriptions typically include operational guidance such as the types of schema matchers and wrappers that work best for the resource or exceptions that must be used for schema heterogeneities, e.g., the pair Determination Process: Semantic Type under mapping is one such mapping. The meta entries listing validators can be used to determine the credibility of the resource.  
Using ResCom, we are now also able to generate process descriptions for the MiKDB [31] database and MapBase ID mapping system as follows.  
create process MIKDB  
at _[[http://mik.bicinirrh.res.in/mip.php_](http://mik.bicinirrh.res.in/mip.php_)]([http://mik.bicinirrh.res.in/mip.php_](http://mik.bicinirrh.res.in/mip.php_))  
access browser  
postfix /mip.php/  
accepts filter (  
_Phenotype_ String) (  
_Symbol_ GeneSymbol primary key,  
_ChrLoc_ string,  
_Disease_ string  
);  
and  
create process MapBase  
at _[[https://www.mapbase.smartblab.org_](https://www.mapbase.smartblab.org_)]([https://www.mapbase.smartblab.org_](https://www.mapbase.smartblab.org_))  
access browser (  
_Symbol_ GeneSymbol primary key,  
_GenelD_ string  
);
## 2 Beidou-Binocular Cooperative Navigation Model
### Binocular Vision Recognition and Perception Module
#### 2.2.1 Principle of Binocular Vision  
The binocular vision system consists of a pair of synchronized cameras. By capturing left and right images of the same scene, the system calculates depth information for the three-dimensional scene based on the disparity principle. Binocular vision generates dense depth maps that provide three-dimensional spatial information for path planning and obstacle detection. Additionally, by combining the binocular vision system with artificial intelligence algorithms (such as YOLO, DeepLab, etc.), objects in the scene can be detected and segmented, enabling the recognition of static and dynamic obstacles.
## 3 Results  
### Securing the Positive Controls  
The average nucleic acid concentrations were 169.0 ng/\(\mu\)L (the average A260/A280 ratio, 1.91) among the six bacteria and 122.0 ng/\(\mu\)L (A260/A280 ratio, 1.71) among 25 total DNAs. The denitrification-related genes (_napA_, _narG_, _nirK_, _nirS_, _norB_ (_cnorB_ and _qnorB_), and _nosZ_) were analyzed for the PCR levels and non-specific amplification using two types of nucleic acids (bacteria chromosomal DNA and total DNA from the surface soil samples). For the other N cycle-related genes (_amo_ (_amoA_, _amoB_, and _amoC_), _hao_, _hzo_, _hzs_, _nifH_, _nirB_, _nrfA_, and _nxr_), we used total DNA extracted from the surface soil samples. The positive controls were secured for 13 of the detailed genes, excluding _amoC_, _hao_, _hzo_, _nirB_, and _nxrB_ (Table 2 and Supplementary Figures S1-S14).
## 3 Results
### Bioreactor Analysis  
This analysis served to evaluate the stress experienced by hMSCs during the dynamic culture, as a function of the scaffold physical and mechanical characteristics. Velocity streamlines during scaffold/holder system movement on the surface of the scaffold in contact with the fluid were evaluated during the culture time. When holder and scaffold were at start positions (at the inlet and at the end of the movement), a vortex occurred in the areas above and below the scaffold (Figure S2). Moreover, the maximum value of velocity appeared on the gap between the rings and tube walls. These results were in agreement with real situation. To analyze flow-dynamics on the scaffold, wall shear stress (WSS) was considered during the new-tissue growth, defined according to the following formula:  
\[WSS=\sqrt{(\tau_{x})^{2}+\left(\tau_{y}\right)^{2}+\left(\tau_{z}\right)^{2}} \tag{1}\]  
being \(\tau_{x}\), \(\tau_{y}\) and \(\tau_{z}\) the viscous stresses along the \(x\), \(y\) and \(z\) directions, respectively. The \(WSS\) is the friction force generated by the ring motion on the surface of the scaffold in contact with the fluid, which, thus, affects cell growth. As the rings go up in their movement \(WSS\) increases (Figure S2a). Velocity field, as a result of fluid-structure simulation, is given in Figure S2c, showing similar vortex formation compared with velocity field resulting from fluid-dynamic simulation (Figure S2b), but also the higher value of velocity in the area below the scaffold. Mechanical stress increased along with the rising motion of the scaffold/holder system. As an example, the maximal von Mises stress in *PCL scaffold increases with time as follows: (t = 1.0 s) \(>\) (t = 0.5 s) \(>\) (t = 0 s). HA/*PCL showed a similar trend, but the area sustaining the von Mises stress in the bottom position was wider than *PCL. A comparison of von Mises stresses for same positions showed that during its motion in the bioreactor, the *PCL scaffold is subject to a maximum stress that is double than that of HA/*PCL (Table 2). A von Mises stress map is shown in Figure 7.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Time (s)** & **0** & **0.5** & **1.0** \\ \hline
*PCL & 0.0112 & 0.0626 & 0.0896 \\ HA/*PCL & 0.0070 & 0.0308 & 0.0411 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Von Mises stress (Pa) on the scaffold surfaces as calculated via Comsol multiphysics simulation.
## 2 Geologic Background and Methods
### Partitioning of Crust-Mantle Heat Flow
In the shallow part (0-5 km), Cenozoic river and lake facies gravel and mudstone dominate, forming a low-speed layer (\(\mathrm{V_{p}<5.0~{}km/s}\)), reflecting weakly consolidated sedimentary characteristics. The middle and deep parts (5-22 km) consist of Triassic shallow metamorphic sandstone and granite intrusions, showing alternating high-speed and low-speed features, likely related to multiple tectonic-magmatic activities. Between 22 and 41 km depth, a significant low-speed layer (\(\mathrm{V_{p}\approx 5.8\text{--}6.2~{}km/s}\)) exists, which is hypothesized to be a partially molten layer in the crust, closely related to the regional high geothermal heat flow background. The anomalous geothermal heat flow density in the Qinghai-Tibet Plateau (Gonghe Basin) corresponds well with the low-speed layer in the crust, which, to some extent, confirms the high-temperature characteristics of the low-speed layer. The partially molten layer within the Gonghe Basin crust may be related to the upwelling of the deep asthenosphere, where thermal material undergoes decompression melting and rises along the Kunlun fault zone. Some of the molten material intrudes into the extensional stress zone of the Gonghe Basin, heating the crustal rocks and causing partial melting. This model needs to consider the thermal conductivity effects of deep large faults. The movement of deep heat sources along the fault may be one of the major causes of heat damage along the tunnel. The profile in Figure 2 reveals the multi-cyclical characteristics of the sedimentary sequence in the Gonghe Basin: the Pliocene-Pleistocene is mainly characterized by lacustrine fine-grained deposits, indicating a static water environment; the Holocene is marked by the development of alluvial and flood-debris conglomerates, indicating rapid accumulation during a period of tectonic activity. This cyclical process is coupled with the periodic uplift and erosion of the northeastern Qinghai-Tibet Plateau. The distribution of the low-speed layer (22-41 km) matches the spatial location of the boundary faults of the Gonghe fault basin (such as the Qinghai-Nanshan Fault and the Lajishan Fault), fitting the typical model of crustal extension and thinning caused by asthenospheric upwelling in rift-type tectonics. Moreover, the alternating arrangement of high-speed and low-speed bodies may reflect the interplay between magmatic intrusion and sedimentary filling during the evolution of the rift. Previous studies suggest that the Gonghe Basin formed in a rifting environment in the early Neogene [13], with its northern margin (the Qinghai-Nanshan Fault) exhibiting normal fault properties, and multiple episodes of basalt eruption within the basin, further supporting the rift-type tectonic hypothesis.
# Multi-Sensor System for Analysis of Maneuver Performance in Olympic Sailing
## 2 System Setup, Test, and Data Processing Method
### Field Test  
The system was tested with a male elite sailor, age 22, in Trondheim Fjord, leaving from Skansen Marina, heading north into the open water to minimize turbulence from surrounding land formations. The laser was escorted by a rib to document the test, in coordination with the sailor throughout the test. The sailor was instructed to perform a normal training session, focusing on consistent performance. After a warm-up session, the sequence of upwind tacks was initiated before returning to the harbor in downwind and broad reach. Table 1 shows the average weather conditions from the test period. Weather data was fetched from the research weather station of The Foundation for Industrial and Technical Research (SINTEF) [34]. The study was conducted in accordance with the Declaration of Helsinki and approved by the Ethics Committee of Norway (ID: 661300, approved 16 January 2025).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Metric** & **Reading** \\ \hline Mean Wind Speed [m/s] & 9.2 \(\pm\) 0.75 \\ Gust [m/s] & 10.4 \(\pm\) 0.17 \\ Mean Wind Direction [\({}^{\circ}\)] & 339.9 \(\pm\) 4.85 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Weather conditions during field test, given in mean \(\pm\) standard deviation (SD).
# Palm Date Leaf Clipping: A New Method to Reduce PAPR in OFDM Systems
## 5 Simulation Results  
To evaluate the performance of proposed Palm Clipping technique, we considered the OFDM system shown in Figure 6 and set the parameters as listed in Table 1. Since it known that the PAPR problem becomes worse for higher-order Quadrature Amplitude Modulations (QAM), we considered 16-QAM and 64-QAM as modulation schemes, in addition to 4-QAM. The number of subcarriers N was set to 64, and the number of OFDM symbols generated was 10,000. The performance of the proposed method is described by the CCDF curves of PAPR and BER degradation. The two principal parameters are also considered, namely CR and the smoothness parameter \(\beta\).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Modulation type & 4-QAM, 16-QAM, and 64-QAM \\ Number of symbols & 10,000 \\ Number of subcarriers \(N\) & 64 \\ Channel Model & AWGN \\ Clipping Ratios (CR) in dB & 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, and 10 \\ Smooth \(\beta\) & 0.5 and 5 \\ HPA type & SSPA \\ HPA parameter \(p\) & 2 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation parameters. QAM: Quadrature Amplitude Modulation.  
Figure 8: Palm Clipping function \(P_{\beta}^{A}(r)\) as a function of \(\beta\) with \(A=10\).  
To validate our method, we firstly studied the PAPR reduction performances by varying the modulation type (4-QAM, 16-QAM, and 64-QAM), CR, and smoothness parameters, as mentioned in Table 1. Secondly, we analyzed the BER with the same conditions, with and without HPA, in order to evaluate to the output back-off of the power amplifier. Finally, we compared our method with other PAPR reduction methods.