esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Concluding Remarks  
The significance of two distinct thermal energy modulations in the Marangoni convective flow of nanomaterial across an infinite disk is examined numerically. The CCF is utilized in the thermal energy analysis. Several mathematical models for reduced Nusselt number and Sherwood number are proposed. Here are some key findings from this work:  
* The strength of the Lorentzian body increases the thermal and solutal layer thickness but decreases the velocity. This is due to the retardation force exerted by the applied magnetic field.
* The Brownian motion and thermophoresis phenomena improve the heat transfer, but when it comes to the solute, the thermophoresis number increases the mass transfer and the opposite nature is observed for the Brownian number. This is due to the Brownian motion and thermophoresis mechanisms of nanoparticles.
* Thermal energy modulations (ESHS and THS) significantly improve the temperature field, as both modulations supply additional heat into the nanoliquid system.
* A decrease in the thickness of both the thermal and the solute layer is observed as the Lewis number increases.
* Marangoni convection progresses the velocity of the nanomaterial. This is due to the surface tension at the disk surface.
* The Nusselt number is higher in the presence of the Marangoni convection.
* The Nusselt number is found as a maximum for the effect of nanoparticles.
* Sherwood's number improved by increasing the Lewis number and heat source parameter.
* Quadratic regression is more important than the linear model for both the reduced Nusselt number and the Sherwood number  
**Author Contributions:** Conceptualization, M.B. and G.L.; methodology, S.N.; software, S.N.; validation, A.A., T.M. and M.B.; formal analysis, M.B.; investigation, G.L.; resources, A.A.; data curation, T.M.; writing--original draft preparation, S.N.; writing--review and editing, M.B. and G.L.; visualization, T.M.; supervision, T.M.; project administration, T.M.; funding acquisition, G.L. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
**Nomenclature**  
\begin{tabular}{l l} \(A\) & characteristic temperature (K) \\ \(B\) & characteristic nanoparticles volume fraction \\ \(B_{0}\) & magnetic field intensity (telsa) \\ C & Nanoparticles volume fraction \\ \(c_{p}\) & specific heat \\ \(C_{\infty}\) & ambient nanoparticle volume fraction \\ \(C_{w}\) & nanoparticle volume fraction at the surface of the disk \\ \(D_{B}\) & coefficient of Brownian diffusion \\ \(D_{T}\) & coefficient of thermophoretic diffusion \\ \(F\), H & dimensionless velocities along \(z\) and \(r\) direction \\ \(Ha\) & Hartmann number \\ \(k\) & Thermal conductivity \\ \(Le\) & Lewis number \\ \(Ma\) & Marangoni number \\ \(Nt\) & Thermophoresis number \\ \end{tabular}
# Molecular Techniques and Target Selection for the Identification of _Candida_ spp. in Oral Samples
## 3 Molecular Identification of _Candida_ spp.
### Nested PCR  
A nested polymerase chain reaction was designed to increase PCR sensitivity by re-amplifying PCR products. Two sets of primers are used in two successive reactions, where the second set intends to amplify a second target within the PCR product from the first run [71]. To limit the amplification of non-specific products, the first reaction allows amplification for a low number of cycles. The second primer set must amplify exclusively the target product from the first amplification and not non-specific products. Nested-PCR was used by [PERSON] and colleagues to amplify the DNA topoisomerase II genes of _C. kefvr_, _C. krusei_, _C. tropicalis_, _C. dubliniensis_, _C. parapsilosis_, _C. guillermondii_ and _C. lusitaniae_[72]. The DNA topoisomerase II gene sequence includes highly conserved regions separated by species-specific regions [61]. [PERSON] et al. [72] conducted a nested-PCR amplification, in which genomic DNA was amplified with a degenerated primer pair (Table S2), followed by the additional amplification using primer mixtures, to improve specificity.  
Nested PCR requires more reagents than conventional PCR, an extra set of primers, and one extra round of agarose gel electrophoresis, becoming a costly and time-consuming method. Additionally, a second amplification reaction increases the risk of sample contamination.
## 2 Materials and Methods
### Perform Evaluation  
Statistical parameters allow assessing the level of confidence and reliability of the estimates of the model. The performance of the empirical equations modeled is evaluated using R\({}^{2}\), RMSE, MSE, and MAE indices.  
The Coefficient of Determination (R\({}^{2}\)) measures the proportion of the variability in the dependent variable that is explained by the independent variable(s) in the model. RMSE provides an estimate of the average magnitude of the errors between estimated and observed values. Similar to RMSE, MSE is commonly used in optimization problems and is sensitive to large errors. MAE measures the average absolute errors, providing a more interpretable scale than MSE. Finally, STD is widely used as a measure of variability or dispersion.  
In addition to the accuracy evaluation, it is necessary to study the computational cost of the equations in terms of time and memory, and these equations are tested with various input sizes using different values. Execution time is recorded, and memory usage is tracked to measure peak memory consumption.
# Recent Progress on Vegetation Remote Sensing Using Spaceborne GNSS-Reflectometry
## 3 Existing Problems
### Utilization of Observation Angle for Vegetation Biomass Monitoring  
A significant advantage of GNSS-R technology is multi-angle observation. Navigation satellites continuously emit direct signals, which are received at various angles, and the signals reflected by the ground surface can produce scattered signals at various scattering zenith angles and scattering azimuth angles. With the improvement and promotion of receiver performance, more of these scattered signals on the ground are likely to be received to a large extent in the future. The GNSS-R technology in ocean surface is relatively early, and the angle processing is usually normalized, that is, all incident angles are normalized to intermediate angles. Many studies on the land surface have followed this processing method [59; 60]. However, its limitation is that GNSS-R has not exerted an effective practical value on the massive data obtained by multi-angle and omni-directional observation of ground objects. Due to the anisotropy of land surface parameters, there are obvious differences in the scattering characteristics at different angles. How to mine angle information, develop parameterized models suitable for different angles, and improve the inversion accuracy based on multi-angle information is an important aspect of GNSS-R for vegetation biomass monitoring.
# Within and Across-Language Comparison of Vocal Emotions in Mandarin and English
## 3 Results
### Within-Language Comparison of Vocal Emotions
#### 3.1.2 English
Therefore, in this part, we focused only on how each cue performed among different emotions within each language and only compared the ordering of five emotions for each cue in the two languages. For instance, anger had the highest pitch-related measures and sadness had the lowest (except for mean pitch) in Mandarin. However, in English, happiness was ordered the highest in terms of pitch-related cues and neutral was the lowest.
## 3 Forecasting Approach
### Performance Indexes  
#### 3.2.1 Performance Criteria of Stage I (Clustering)  
The effectiveness of the clustering process was measured using two clustering quality indices (\(CQI_{s}\)) based on the Silhouette function (\(S_{i}\)). The silhouette value determines the degree of equality in clusters between a selected pattern and the other patterns that conform a certain cluster compared with the patterns in other clusters. The \(S_{i}\) value for each pattern is between \(-1\) to \(+1\) and is defined in Equation (12):  
\[S_{i}=D_{i}-d_{i}/\max(d_{i},D_{i}), \tag{12}\]  
where \(D_{i}\) is the minimum average distance from one pattern of a cluster to another pattern in another cluster and \(d_{i}\) is the average distance in the own cluster from one pattern to the rest of patterns. The two \(CQI_{s}\), which are based on the mean and the sum of the silhouette values of all clusters respectively, are collected in Equations (13) and (14). The lower of these values, the worst of the clustering results:  
\[QI_{1}=(\overline{S_{i}}), \tag{13}\]  
\[QI_{2}=\sum S_{i}. \tag{14}\]
# Mechanical Structure Design and Experimental Study of Gamma-ray Monitor for Small Satellite Payload
## 4 Simulation and Experimental Results
### Dynamical Analysis
#### 4.2.3 Random Vibration Analysis
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Measurement Points**} & \multirow{2}{*}{**Categories**} & \multicolumn{2}{c}{**X-Direction**} & \multicolumn{2}{c}{**Y-Direction**} & \multicolumn{2}{c}{**Z-Direction**} \\ \cline{3-8}  & & **Grms (g\({}^{2}\)/Hz)** & \multirow{2}{*}{**Magnification**} & **Grms (g\({}^{2}\)/Hz)** & \multirow{2}{*}{**Magnification**} & **Grms (g\({}^{2}\)/Hz)** & **Magnification** \\ \hline \multirow{3}{*}{1} & Analysis & 50.35 & 4.76 & 30.16 & 2.85 & 57.90 & 5.48 \\  & Test & 56.16 & 5.31 & 29.31 & 2.77 & 54.29 & 5.13 \\  & Error & & \(-10.4\%\) & & 2.9\% & & 6.6\% & \\ \hline \multirow{3}{*}{2} & Analysis & 57.67 & 5.45 & 29.4 & 2.78 & 42.90 & 4.06 \\  & Test & 57.04 & 5.39 & 25.81 & 2.44 & 47.31 & 4.47 \\  & Error & & 1.1\% & & 13.9\% & & \(-9.3\%\) & \\ \hline \multirow{3}{*}{3} & Analysis & 28.93 & 2.74 & 30.10 & 2.85 & 29.81 & 2.82 \\  & Test & 25.32 & 2.39 & 33.16 & 3.14 & 29.69 & 2.81 \\ \cline{1-1}  & Error & & 14.2\% & & \(-9.2\%\) & & 0.4\% & \\ \hline \hline \end{tabular}
\end{table} 
Table 9: PSD response after model correction. Figure 13: Sinusoidal characteristic sweep frequency map of measurement points before and after X, Y, Z random test: (**a**) Measurement point 1 in X-direction. (**b**) Measurement point 2 in X-direction. (**c**) Measurement point 3 in X-direction. (**d**) Measurement point 1 in Y-direction. (**e**) Measurement point 2 in Y-direction. (**f**) Measurement point 3 in Y-direction. (**g**) Measurement point 1 in Z-direction. (**h**) Measurement point 2 in Z-direction. (**i**) Measurement point 3 in Z-direction.
## 3 Model Description
### Comparison of Data Distribution  
In order to explain the reconstruction ability of TCReC based on the characteristics of the reconstructed data itself and compare it with other reconstruction algorithms, we employed the t-SNE algorithm to perform feature dimensionality reduction to 2D on the original traffic data, reconstructed traffic data from different algorithms, and traffic data with 70% masked proportion (to simulate scenarios of packet loss), and visualized theseon scatter plots. The scatter plot represents the relative positions between different traffic types as well as the clustering degree of each individual traffic flow. Different colors are used to represent different traffic types, and each point in the plot represents a traffic flow. From multiple scatter plots in Figure 6, we observed the following points:  
1. The distribution of the data flows after 70% masking is significantly altered and shows substantial differences from the distribution of the original data.
2. Compared to the scatter plot of the original traffic flows, each traffic flow reconstructed by TCReC is distributed in positions that are close to the original ones.
3. The clustering degree of each type of flow reconstructed by TCReC is also similar to that of the original flows.
4. The reconstructed data from other algorithms exhibit varying degrees of data distribution shift, while some algorithms achieve an overall distribution that is close to the original traffic, there are significant changes in the relative positions of each type of flow's clustering with respect to other types. We will provide a detailed discussion on this aspect in the experimental section.  
It should be noted that, even though the t-SNE algorithm can not represent precisely the actual distribution of the high-dimensional feature of the traffic data after the reduction to a two-dimensional plane, we can still compare different reconstructed data with the original data and observe the similarity and clustering degree of the traffic data intuitively.
## 3 7R Data Value Framework in Practice
### Stakeholder Relationships, Business Cases and Data Strategy
#### 3.1.3 R3: Revealing Data  
The datasets were chosen to support the application scenarios defined above aimed at a generic prototype as a showcase. At least three datasets were needed: patents, scientific articles and funding opportunities. For each of the three initial datasets, its data size, format and standard had to be taken into account. Patents and publication articles are very large datasets provided as XML files with different schemas. Funding opportunities are provided as spreadsheet without reusable schemas. The concepts behind the datasets had to be understood by analyzing the XML documents or spreadsheets and other documentation available from the data publisher. For patents, the European Patent Office and, for funding opportunities, EUresearch provided additional information by e-mail.  
* Patents: A patent document has metadata attributes, such as the inventors, patent application status, publication date, country, claims and description. There are four major classification systems for patents. These taxonomies come with around 150,000 concepts. Patent documentscan be downloaded from the European Patent Office or the United States Patent and Trademark Office in XML format. MAREC (Matrixware Research Collection) contains a corpus of over 19 million patents in XML format from the World Intellectual Property Organization (WIPO);
* Publications: PubMed is an archive of biomedical and life sciences articles published by different journals. The National Library of Medicine at the U.S. National Institute of Health maintains the archive. The articles are freely accessible from the PubMed website according to its release policy. A free license to use this dataset was requested and approved. The dataset is divided in folders, each containing articles from a journal. The XML document contains bibliographic information, such as title, abstract, authors and affiliation, license, citations, classifications and identifiers (DOI, digital object identifiers);
* Funding opportunities: The European Commission funds research and innovation programs through specific calls to which companies and research organizations apply. A call belongs to challenges that target one or more objectives. A company or organization must search in the portal among these objectives to which call it wants to participate. Each call has a title or number, description, publication date and deadline, area, funding scheme, budget allocated and topics. EUresearch, a Swiss organization that supports companies and institutions applying to research grants, has provided the dataset as a schema-free spreadsheet.
# A Survey of Precision Formation Relative State Measurement Technology for Distributed Spacecraft
## 2 Research Status of the Precision Formation of Distributed Spacecraft Project
### Prisma  
PRISMA (Prototype Research Instruments and Space Mission Technology Advancement) [6] was launched on 15 June 2010 and was funded by the Swedish National Space Board. It consisted of two microsatellites, Mango (95 kg) and Tango (50 kg). The mission was to verify the M-level autonomous formation, approach, and rendezvous stop technology, and to perform precise control at DM-level in a sun-synchronous orbit at an orbital altitude of about 700 km. GNSS signals are unreliable in this case, due to the altitude of the formation flight orbit. The spacecraft adopted the formation flight RF system, developed by CNES (French Space Agency), to provide stable autonomous measurements during the mission, as shown in Figure 3, to realize formation-flying technology verification and ground detection [7].  
For the first time, the project carried out a dual-orbit spaceborne autonomous formation flying experiment (SAFE) in low-Earth orbit and achieved an innovative relative navigation technology verification of GPS with a vision-based sensor (VBS) and formation flying radio frequency (FFRF). SAFE is divided into four stages, as shown in the Table 2. The  
Figure 3: The relative-state measurement FFRF system; the antenna structure of PRISMA.  
Figure 2: Diagram of the Darwin project. (**a**) Formation shape of Darwin, (**b**) Space orbit schematic of Darwin.  
project used a GPS, FFRF (formation flying radio frequency) sensor, and VBS (vision-based sensor) to measure the distance between satellites. The rough stage distance measurement accuracy was 10 cm, and the effective distance was 30 km; the precision stage distance measurement accuracy was 1 cm, and the effective distance was 250 m.
# A Radiation-Hardened Low-Power SRAM with Enhanced Write Capability for Space Applications
## 3 Implementation Results and Analysis
### Write Capability Comparison  
WWTV is defined as the word line voltage at which the storage nodes Q and QB intersect during a write operation. This dynamic metric reflects a cell's writability and internal drive strength under realistic conditions. Unlike static measures such as Static Noise Margin (SNM), WWTV offers a more accurate indication of write capability, especially in high-speed and low-voltage environments [16].  
Figure 7 compares the WWTV of various SRAM designs, including QCCS, SCCS, SAW16T, HP16T, RHSCC16T, S8P8N, and the proposed EWS-16T cell. Higher WWTV  
Figure 11: Hold power consumption comparison for different SRAM cells.  
indicates stronger internal node controllability, enabling more reliable and efficient writes. As shown, cells with longer write access times generally show lower WWTV, while those with faster write access times achieve higher WWTV, confirming the correlation between write speed and dynamic writability. The proposed EWS-16T, with its dual-access structure and optimized PMOS pull-up ratio, achieves the highest WWTV while also exhibiting the shortest WAT among all comparison cells. These features highlight its excellent internal drivability and write efficiency under dynamic conditions. However, this enhancement in writability achieved by upsizing access and pull-up transistors to increase WWTV also weakens the storage node, making it more susceptible to voltage disturbance during reads and thus reducing RSNM. This trade-off arises because stronger write paths lower the voltage margin necessary for stable read sensing. To address this, careful transistor sizing and selective feedback control are required to improve RSNM without significantly compromising write performance. Experimental results show that EWS-16T achieves a WWTV of 279 mV, which is 3.40\(\times\), 4.04\(\times\), 1.76\(\times\), 1.11\(\times\), 2.20\(\times\), and 1.30\(\times\) higher than QCCS, SCCS, SAW16T, HP16T, RHSCC16T, and S8P8N, respectively. These results confirm its superior dynamic write stability and robustness for SRAM applications.
###### Abstract
The experimental verification of the proposed controller is carried out on a laboratory overhead crane by testing the NMPC performance at different operating points in the presence of external disturbances and comparing the results with those of a discrete-time feedback controller developed using the pole placement method. The main contributions of this paper are as follows:  
1. A regularized genetic programming data-driven modeling approach is used to develop an NMPC scheme for an underactuated overhead crane system. 2. The proposed data-driven modeling approach and model-based control scheme are experimentally verified under different operating conditions and in terms of robustness with respect to external disturbances. The rest of this paper is organized as follows: Section 2 introduces modeling assumptions, provides the identification procedure developed using GP with sparse regression, and describes the NMPC control strategy with the PSO solver. The hardware experimental setup and the results are presented in Section 3, while the final conclusions are made in Section 4.
# Mitigating the Impact of Noise on Nonlinear Frequency Division Multiplexing +
## 7 Discussion  
All the NFDM systems proposed so far have an important limitation: their performance decays when the signaling time is increased--the latter being a necessary condition to achieve a reasonable spectral efficiency in the presence of the guard time required by the NFT boundary conditions. This happens even in the ideal scenario considered in this work and, in fact, even over a simple AWGN channel. Thus, before delving into more realistic and detailed scenarios, it is fundamental to solve this critical issue.  
In this work, we have proposed and studied several different modulation and detection strategies for NFDM systems, which can greatly improve their performance and alleviate the above-mentioned problem--unfortunately, without fully solving it. In the following, we briefly touch upon some  
Figure 8: Peak performance of NFDM systems with different modulation and detection strategies, compared to a linearly modulated system with burst (solid) or continuous (dashed) transmission.  
important aspects which have not been considered in our investigation, discussing their potential impact and their relevance in future studies on NFDM.