esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methods
### [PERSON]'s Modulus Reconstruction Algorithm and Data Processing  
An iterative reconstruction algorithm was used to recover the [PERSON]'s modulus of all the data [33; 42]. In this algorithm, an initial guess of the [PERSON]'s modulus is made before a finite element (FE) model of the US field of view is generated, where the loading is provided as prescribed displacement boundary conditions obtained from the measured displacement field. The FE method is run, and based on the stresses from the model and the strain calculated from the US motion tracking, the [PERSON]'s modulus is recalculated. This is repeated until the [PERSON]'s modulus values converge. Figure 1 demonstrates the process with a flow chart. For the synthetic dataset, all generated data, including both pre- and post-STREAL, were used to reconstruct the [PERSON]'s modulus \(\mathbf{\xi}\) images, while this image reconstruction method was also carried out using the ground truth displacements obtained through the FE simulation. The synthetic reconstructed data was processed as follows:  
1. The inclusion-to-background [PERSON]'s modulus ratio was calculated, and a percent difference was calculated between the measured ratio using displacement estimators and the reconstruction performed with the ground-truth displacement. This can provide an idea of the diagnostic power of each estimator, as the ratio is what determines potential malignancy.
2. The Hausdorff distance was calculated between the border of the segmented inclusions of the synthetic data reconstruction and the ground truth reconstruction. This metric was chosen to measure the change in shape of the inclusion.
3. The mean squared error (MSE) between the resultant [PERSON]'s modulus images and the ground truth was calculated.
4. The Young's modulus ratios, Hausdorff distance, and MSE were then statistically compared using an ANOVA test with Bonferroni corrections.
5. Using the same thresholding procedure as in step 2, the specificity, sensitivity, and kappa statistic were calculated for agreement with the ground truth for each displacement estimator.  
For the tissue mimicking phantom and clinical examples, strain images for the axial and lateral images were also generated, along with CNR, SNR, and inclusion to the background [PERSON]'s modulus ratios. The CNR and SNR were calculated using two ROIs, one on the inclusion and one in the background.
## 4 Basics of Voronoi Diagram
### Formal Definition of Voronoi Diagram  
Let denote \(\mathbf{S=\{s_{i},s_{2},...,s_{n}\}}\) as the set of points, called \"sites\" or \"generators\", in a space \(\mathbf{E}\) (\(\boldsymbol{R^{2}}\)_or \(\boldsymbol{R^{3}}\)_). Also, consider \(\mathbf{s_{i}}\) and \(\mathbf{s_{j}}\) as two sites that belong to \(\mathbf{S}\). The dominance (**DOM**) of \(\mathbf{s_{i}}\) over \(\mathbf{s_{j}}\) may be formulated as the subset of the space \(\mathbf{E}\) being at least as close to \(\mathbf{s_{i}}\) as to \(\mathbf{s_{j}}\). This dominance may be formulated as follow [32]:  
\[DOM\big{(}s_{i}\,,s_{j}\big{)}=\ \big{\{}\ p\in E\ \big{|}\ Dist(p\,,\,s_{i})\ \leq\ Dist \big{(}p\,,\,s_{j}\big{)}\big{\}} \tag{1}\]  
where: **Dist (a,b)** is the Euclidian Distance between point a and b and \(\mathbf{p}\) is a point that belong to the space \(\mathbf{E}\).  
From the foregoing, a Voronoi region (**VOR**) generated by a site \(\mathbf{s_{i}}\) is the result of the intersection of all the dominances of \(\mathbf{s_{i}}\) over the remaining sites in \(\mathbf{S}\). This means [32]:  
\[VOR(s_{i})=\ \bigcap_{s_{j}\in S-\{s_{i}\}}DOM(s_{i}\,,s_{j}) \tag{2}\]  
Hence, the Voronoi Diagram (**DIAVOR**) generated from a set of sites \(\mathbf{S=\{s_{i},s_{2},...,s_{n}\}}\) is the collection of all Voronoi regions that divides the entire space:  
\[VOR(s_{i})=\ \bigcap_{s_{j}\in S-\{s_{i}\}}DOM(s_{i}\,,s_{j}) \tag{3}\]  
Figure 4 illustrates a Voronoi Diagram that results from a set of sites:  
In a Voronoi Diagram, each point on a Voronoi edge is equidistant from the two sites whose corresponding regions share this edge. Also, each vertex is equidistant from the nearest three sites that generate Voronoi regions [32]. In addition, the principles of Voronoi Diagram may be extended to different types of sites or \"generators\" such as lines, polygons and weighted features (point, line, polygon) instead of ordinary points [33]. Figure 5 shows Voronoi Diagrams generated from unweighted lines and polygons (A, C) and from weighted lines and polygons (B, D) that have the same shape as (A, C). The weight used in Figure 5 corresponds to the line length for B and the polygon area for D. The grayscale colors correspond to the distance (or weighted distance in case of B and D) between each point  
Figure 4: A Voronoi Diagram generated from a set of “sites”.  
and the nearest generator feature [33]. For more details about the mathematical principles and applications of Voronoi Diagrams, the reader is referred to [30].
_Appl. Sci._**2013**, \(3\), 624-635; doi:10.3390/app3030624  
_applied sciences_  
**ISSN 2076-3417**  
www.mdpi.com/journal/applsci  
Evaluation of Rapid Stain IDentification (RSIDTM) Reader System for Analysis and Documentation of RSIDTM Tests  
**[PERSON] *, [PERSON], [PERSON], [PERSON] and [PERSON]**  
Independent Forensics, 500 Waters Edge, Suite 210, Lombard, IL 60148, USA;  
E-Mails: [EMAIL_ADDRESS] (A.K.); [EMAIL_ADDRESS] (J.B.O.); [EMAIL_ADDRESS] (P.W.B.); [EMAIL_ADDRESS] (K.A.R.)  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +1-708-234-1200; Fax: +1-708-978-5115.  
_Received: 14 June 2013; in revised form: 10 July 2013 / Accepted: 29 July 2013 / Published: 5 August 2013_  
**Abstract**: The ability to detect the presence of body fluids is a crucial first step in documenting and processing forensic evidence. The Rapid Stain IDentification (RSIDTM) tests for blood, saliva, semen and urine are lateral flow immunodagraphic strip tests specifically designed for forensic use. Like most lateral flow strips, the membrane components of the test are enclosed in a molded plastic cassette with a sample well and an observation window. No specialized equipment is required to use these tests or to score the results seen in the observation window; however, the utility of these tests can be enhanced if an electronic record of the test results can be obtained, preferably by a small hand-held device that could be used in the field under low light conditions. Such a device should also be able to \"read\" the lateral flow strips and accurately record the results of the test as either positive, _i.e._, the body fluid was detected, or negative, _i.e._, the body fluid was not detected. Here we describe the RSIDTM Reader System--a ruggedized strip test reader unit that allows analysis and documentation of RSIDTM lateral flow strip tests using pre-configured settings, and show that the RSIDTM Reader can accurately and reproducibly report and record correct results from RSIDTM blood, saliva, semen, and urine tests.  
**Keywords:** Rapid Stain IDentification (RSIDTM) Reader System; RSIDTM Blood Test; RSIDTM Saliva Test; RSIDTM Semen Test; RSIDTM Urine Test; accuracy; reproducibility
# High Performance GAA SNWT with a Triangular Cross Section: Simulation and Experiments
## 4 Device Characteristics  
Figure 8a is an SEM image of multiple suspended silicon nanowires formed by the TMAH wet etching technique. Each silicon nanowire has good uniformity. Figure 8b is a TEM cross-sectional view of the silicon nanowire after the gate oxidation process and gate material deposition, which turned out to be an inverted triangle, and the angle between the self-stopped (1 1 1) and the surface crystal plane is close to the theoretical 54.7\({}^{\circ}\), almost reaching an equilateral triangle apex.  
Figure 7: Process for fabricating P-type GAA SNWT by the TMAH wet etching technique on a bulk silicon substrate.  
Figure 9a,b shows the transfer and output characteristics of a P-type GAA SNWT with a triangular cross section. The nanowire had a line width of 20 nm and a gate length of 140 nm. Under a bias voltage of 1.0 V, the on-state current normalized by the line width reached 1402 \(\upmu\)A/\(\upmu\)m, the leakage current was limited to 0.4 nA/\(\upmu\)m, and the on/off ratio exceeded 10\({}^{6}\). Furthermore, the device had a low subthreshold slope of 85 mV/dec and DIBL of 63 mV/V.  
Figure 10 illustrates the trends of subthreshold slope, DIBL, and threshold voltage with different gate lengths for the triangular GAA SNWTs. From Figure 10a, it can be found that the subthreshold slope remained almost constant as the gate length decreased, while the DIBL increased slightly. The reason why DIBL degraded with the gate length scaling more obviously is that the polysilicon gate overlapped the trench, resulting in the device's source and drain extension regions not being a nanowire structure, but the traditional planar structure. Hence, the high level drain voltage can impose a greater influence on the channel potential. In Figure 10b, the threshold voltage of the triangular GAA SNWTs in the linear and saturated region is almost constant as the gate length is varying, indicating that it had an excellent SCE suppressing capability.  
Figure 8: (**a**) SEM image of multiple suspended silicon nanowires formed by TMAH etching; (**b**) TEM image of silicon nanowires after gate oxidation and polysilicon gate deposition.  
Figure 9: (**a**) Transfer characteristic; (**b**) output characteristic of P-type triangular GAA SNWT with D = 20 nm and L\({}_{\text{g}}\) = 140 nm.
## 4 Discussion
Much like the current direction of FMCW radar systems for vital sign detection, this system is contactless which allows for more flexible subject monitoring while maintaining measurement accuracy, and the design concepts can be incorporated at higher frequency bands. Moreover, the radar demonstrates that respiration measurement accuracy is not lost when implementing phase modulation, allowing for multiple PMCW radar systems to share the same frequency space, additional information to be encoded onto the phase modulation, and expansion into a monostatic MIMO system. For each of the three radar modes, CW, PSK, and PMCW, the demodulated \(I/Q\) response peak frequency strongly aligns with the corresponding peak frequency of the respiration belt over the 60 s measurement window. Here, the subject's breathing was voluntary and controlled, but respiration rates will vary depending on factors such as but not limited to stress levels, mood, and current health [25]. Speaking also causes breathing to be abnormal. However, these variations will affect the frequency spectrums of the measurements from both the respiration belt and radar systems equally, regardless of the modulation method used. Measuring hand gestures has also been explored with FMCW radar systems [7; 8; 9], extended to UWB radar systems [22], expanded to sign language recognition using UWB radar systems [23], and demonstrated using passive radar systems [17]. PMCW gesture and event recognition, however, are still emerging [36]. The spectrogram responses of the four hand gestures measured here show that the low-frequency Doppler information is maintained across both PSK and MLS-based PMCW radar modes. This is encouraging as the low-frequency modulation used in this work can be expanded to CDMA-MIMO radar systems, or joint-radar communication systems, which look to introduce additional information to the carrier wave. Both would allow for more complex clinical research through encoding the radar with identifying patient information, multipatient monitoring, and 3D patient vital sign and gesture measurements. The radar system detailed here also allows for future improvements in FPGA and DAQ systems to be incorporated. For this design, the microwave structures can be implemented for higher carrier frequencies, and the BPS reflective loads can be modified to allow for faster phase switching. The BPS inputs can be controlled with faster FPGAs as the technology improves, and the baseband response measurements and processing can be moved onto the FPGA to accommodate single-bit PMCW processing used by the PMCW SoC radar systems.
# Simulation of the Infiltration of Fractured Rock in the Unsaturated Zone
## 4 Results
### Impact of Fracture Aperture Variations on Wetting Front Velocity  
Clearly, a two-dimensional model cannot resolve processes that operate in the plane of fractures, like the counter-current flow of water and air which was nevertheless indicated by the results obtained from model TUFF2D. Moreover, spatial aperture variations in the fracture cannot be represented.  
The three-dimensional constant aperture model TUFF3D displays a behaviour largely similar to TUFF2D (Figure 20): water spontaneously imbibes into the soil layer before infiltrating further downward through the fractures. The wetting front in the constant aperture fracture is sharp and stable, resulting in a kind of sheet flow.  
By contrast, in the variable aperture model TUFF3D-VA, an unstable saturation front evolves in the fracture (Figure 20). Water preferentially flows along the constrictions, trapping air pockets between these flow zones. Further downward, the water eventually also occupies fracture segments with a wider aperture, developing lobes that infiltrate faster than the average velocity of the wetting front. In constant-aperture TUFF3D, the average wetting front velocity is 1.75 mm/s, but in the variable aperture model TUFF3D-VA it reaches 1.93 mm/s. Co- and counter-current flow patterns in the rock matrix are similar to the ones seen in the low rainfall intensity scenario of the two-dimensional model, (Figure 21).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Regime** & **Fracture 1** & **Fracture 2** & **Fracture 1** & **Fracture 2** \\  & \(\mathbf{S}_{rw}^{m}\) = 0.08 & \(\mathbf{S}_{rw}^{m}\) = 0.08 & \(\mathbf{S}_{rw}^{m}\) = 0.45 & \(\mathbf{S}_{rw}^{m}\) = 0.45 \\ \hline Early-time & 0.00077 & 0.00077 & 0.00077 & 0.00088 \\ Late-time & 0.00021 & 0.00023 & 0.00025 & 0.00030 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Average wetting front velocity (m/s) in the fractures in model TUFF2D, low precipitation rate (case A).  
Figure 19: Wetting front position in 0.5 mm (Fracture 1) and 1.1 mm-aperture (Fracture 2) fracture sets of model TUFF2D as a function of time. (**a**) low rainfall intensity, and (**b**) high intensity scenario. The reference depth 0.0m-is that of the soil-bedrock interface. Note that the front moves much faster in the model at a matrix water saturation of 0.45 than at a hypothetical residual water saturation (moisture retention capacity) taken as 0.08.  
Figure 20: Model TUFF3D, low rainfall intensity. Saturation profiles at different time steps. Constant aperture model (**left**), variable aperture (**right**).
## 4 Results and Discussion
### Experimental Results
The overall average recognition accuracy comparison of transformer iron-core looseness using different methods is shown in Figure 10. It can be seen from Figure 10 that the accuracy of the method proposed in this paper is improved compared to the shallow CNN model \begin{table}
\begin{tabular}{c c c} \hline \hline
**Signal Type** & **Training Set Accuracy** & **Test Set Accuracy** \\ \hline Before refactoring & 98.98\% & 97.62\% \\ After refactoring & 100\% & 99.52\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Diagnostic results before and after signal reconstruction. Figure 9: Confusion matrix. and the GAF ResNet18 model without a multi-head attention mechanism. Compared with traditional methods, the method proposed in this article not only improves the accuracy of diagnosis, but also realizes an automation process from the signal to the diagnosis result, reducing manual intervention. Finally, a comparison was made between the methods proposed in this paper and the recent literature [26] in the relevant field, which utilized continuous wavelet transform (CWT) and an improved CNN. On the basis of the same dataset, the accuracy of the method is 99.24%, which is slightly lower than that of the method in this paper, which further illustrates the feasibility and superiority of the proposed method.
# A Review of Transformer-Based Approaches for Image Captioning
## 7 Challenges and Future Directions
### Few-Shot and Zero-Shot Learning  
Many of the current state-of-the-art vision-language models involve weakly supervised pre-training and subsequent task adaptation through fine-tuning on the downstream tasks of interest. It often happens that the fine-tuning still necessitates the use of large datasets to achieve good performance. A current research trend is to develop models that require less data for the downstream fine-tuning.  
In few-shot learning, the effort is to develop high-performing models which only require a few training samples in the downstream fine-tuning. A number of works ([103; 143; 144; 145]) have focused on this kind of model. Flamingo [143] takes inspiration from large language models, which perform quite well on few-shot language-only tasks, and applies similar methods to achieve good performance in vision-language tasks. Flamingo achieves state-of-the-art few-shot learning performance in 16 multimodal language and image/video understanding tasks with as few as 32 examples per task.  
When taken to the limit, a situation arises in which a model that is pre-trained can perform competitively without the need for any downstream fine-tuning. This constitutes zero-shot learning and is a more challenging research area. Multimodal vision-language models ([53; 54]) have used large-scale contrastive learning to enable zero-shot generalization with regard to novel downstream tasks. However, current zero-shot models are still mainly effective in limited use cases such as classification [143]. [PERSON] [52] demonstrated a capability for zero-shot caption generation but they noted that their zero-shot captions were very short due to the nature of the pre-training datasets. As more research is carried out these models will improve and will be capable of carrying out more open-ended tasks such as captioning or visual question answering. This will be useful as models will be able to rapidly adapt to diverse and challenging tasks.
## 5 Discussion
### Open Challenges
#### 5.3.1 Time-Varying Environment  
Based on current research, it appears that policy-based algorithms such as RL have better performance for RRM than algorithms that rely on training data such as SL because multibeam systems are dynamic (weather impairment based) and demand variations that require huge training datasets. However, this performance gain is obtained at the cost of online processing time. For this reason, different techniques should be studied to help decrease processing times and improve performance, for example, the implementation of Spiking neural networks (SNNs), which are artificial neural networks that more closely mimic natural neural networks [56].  
On the other hand, hybrid methods could be included where ML techniques are combined with convex optimization techniques or different ML blocks where some blocks are trained offline [30], and others are trained online with system updates and extend the study to NGSO (Non-Geostationary Satellite Orbit) system.
## 1 Introduction
[48] in rice showed an interesting correlation between Sentinel-2 data and yields; such work was performed on a small dataset (<70 ha) and a single year and location. The results supported the industry's utilization of RS commercial platforms (mainly Sentinel-2); however, more studies are needed to validate correlations in different years and locations. [PERSON] et al. [47] reported, in corn and soybean, a critical range of correlations, i.e., 0.67, between the yield and spectral bands in a two-year study in commercial farms. Similarly, [PERSON] and [PERSON] [50] found low correlations in corn, soybean, wheat, and cotton, showing an important threshold value of 0.50 in the Spearman correlation coefficient. Thus, as also reported by [PERSON] et al. [51], when several years are considered, stable correlations could be hard to find and interpret across the time-series. It is supposed that surface reflectance and VI exhibit correlations with crop status and yield; thus, the research gap is placed in simulating operability for agronomic decisions. Are the correlations between Sentinel-2 data and yield significantly the same across years, locations, and growth stages? Do Sentinel-2 data always present the expected behavior for each spectral band and VI? Detailed analysis of the collinearities between spectral bands across growth stages, combined with the classical VI performance, may be crucial to provide a framework for data interpretability. As a consequence, the central hypothesis of the present work is the existence of significant correlations between all Sentinel-2 spectral bands or VIs and yield variability for each growth stage within the field and for a broader data set representative of commercial fields spanning different years and locations. Four years of yield maps acquired by a combine harvester in 404 commercial fields (a total area of 706 ha) distributed in two Spanish locations (Valencia and Seville) were studied. We aim to study such correlations and the collinearity between bands and VIs, seeking the satellite data applicability to improve crop management. The results of the hypothesis would provide a source of information to ensure a more efficient implementation of Sentinel-2 in Spanish rice cultivation. Therefore, this work transfers the interpretability of the data rather than an empirical yield forecast.
_Appl. Sci._**2013**, \(3\), 656-674; doi:10.3390/app3040656  
_Applied sciences_  
**ISSN 2076-3417**  
www.mdpi.com/journal/applsci  
_Review_  
**Sustainable Utility of Magnetically Recyclable Nano-Catalysts in Water: Applications in Organic Synthesis**  
**[PERSON] \({}^{1,\dagger,\ast}\), [PERSON] \({}^{2}\), [PERSON] \({}^{1}\) and [PERSON] \({}^{3,\ast}\)**  
\begin{tabular}{l p{284.5 pt}}
1 & REQUIMTE, Department of Chemistry, Faculdade de Ciencias e Tecnologia, Universidade Nova de Lisboa, Quinta da Torre 2829-516 Caparica, Portugal; E-Mail: [EMAIL_ADDRESS] \\
2 & Jubilant Chemsys Ltd., B-34, Sector-58, Noida-201301, New Delhi, India; E-Mail: [EMAIL_ADDRESS] \\
3 & Sustainable Technology Division, National Risk Management Research Laboratory, US Environmental Protection Agency, MS 443, 26 West Martin Luther King Drive, Cincinnati, OH 45268, USA \\ \({}^{\dagger}\) & Present Address: Regional Centre of Advanced Technologies and Materials, Faculty of Science, Department of Physical Chemistry, Palacky University, Olomouc, 17. listopadu 12, Olomouc 771 46, Czech Republic. \\ \({}^{\ast}\) & Authors to whom correspondence should be addressed; E-Mails: [EMAIL_ADDRESS] or [EMAIL_ADDRESS] (M.B.G.); [EMAIL_ADDRESS] (R.S.V.); Tel.: +420-585-634-385 (M.B.G.); +1-513-487-2701 (R.S.V.). \\ \end{tabular}  
_Received: 20 June 2013; in revised form: 2 October 2013 / Accepted: 17 October 2013 / Published: 25 October 2013_  
**Abstract:** Magnetically recyclable nano-catalysts and their use in aqueous media is a perfect combination for the development of greener sustainable methodologies in organic synthesis. It is well established that magnetically separable nano-catalysts avoid waste of catalysts or reagents and it is possible to recover \(>\)95% of catalysts, which is again recyclable for subsequent use. Water is the ideal medium to perform the chemical reactions with magnetically recyclable nano-catalysts, as this combination adds tremendous value to the overall benign reaction process development. In this review, we highlight recent developments inthe use of water and magnetically recyclable nano-catalysts (W-MRNs) for a variety of organic reactions namely hydrogenation, condensation, oxidation, and Suzuki-Miyaura cross-coupling reactions, among others.  
**Keywords:** magnetically recyclable nano-catalysts; aqueous medium; organic synthesis; sustainable methodologies