esa-sceva/satcom-chunk-collection huggingface dataset:
# Adsorption of Lead and Nickel on to Expanded Graphite Decorated with Manganese Oxide Nanoparticles
## 3 Results and Discussion  
### Material Property  
SEM and TEM images of the prepared materials are presented in Figures 1 and 2. A comparison of the surface morphologies of these figures indicates that in the MONPs-EG composite each graphene sheet of EG was successfully decorated with nano-sized manganese oxide particles without significant change in structure. This is confirmed by the change in elemental compositions of samples before and after depositing manganese oxide on the EG surface (Table 1). The graphene sheets could be wetted by manganese oxide precursors due to the presence of supercritical methanol, which possesses near-zero surface tension and highly diffusive properties. The average diameter of the manganese oxide nanoparticles was measured at 23.5 \(\pm\) 2.7 nm (n = 120). The specific surface area of EG decreasedslightly from 98.55 m\({}^{2}\)/g to 87.60 m\({}^{2}\)/g after the attachment of MONPs (Table 2); this decrease is attributed to augmentation of the material density [39]. Meanwhile, the average pore size increased from 6.20 nm to 13.91 nm, which might be due to the insertion and trapping of MONPs in the narrow pores between graphene sheets of the EG.  
Figure 3 presents XRD patterns of EG, Mn\({}_{3}\)O\({}_{4}\) nanoparticles, and MONPs-EG samples. The XRD pattern of EG exhibited typical diffraction peaks of graphite carbon around 20 = 26.5\({}^{\circ}\) and 54.5\({}^{\circ}\), indicating the crystalline nature of the EG [40; 41]. The MONPs-EG pattern revealed a relatively poor crystallinity in its structures compared to that of EG; the additional peaks at 20 = 18.0\({}^{\circ}\), 29.0\({}^{\circ}\), 31.1\({}^{\circ}\), 32.7\({}^{\circ}\), 36.1\({}^{\circ}\), 50.2\({}^{\circ}\), and 60.0\({}^{\circ}\) are characteristic peaks of Mn\({}_{3}\)O\({}_{4}\) (hausmannite), which can be observed from the XRD pattern of Mn\({}_{3}\)O\({}_{4}\) nanoparticles.  
\begin{table}
\begin{tabular}{c c c c c} \hline \multirow{2}{*}{**Element**} & \multicolumn{2}{c}{**Weight \%**} & \multicolumn{2}{c}{**Atomic \%**} \\ \cline{2-5}  & **EG** & **MONPs-EG** & **EG** & **MONPs-EG** \\ \hline C & 97.25 & 49.43 & 97.90 & 71.30 \\ O & 2.75 & 16.68 & 2.10 & 18.00 \\ Mn & 0 & 33.89 & 0 & 10.70 \\ \hline \end{tabular}
\end{table}
Table 1: Elemental compositions of EG and MONPs-EG.  
Figure 1: Scanning electron microscope (SEM) images of: (**a**) expanded graphite (EG); (**b**) manganese oxide nanoparticles (MONPs)-EG composite.  
Figure 2: Transmission electron microscope (TEM) images of: (**a**) EG; (**b**) and (**c**) MONPs-EG composite at different magnifications.
# Sign Language Sentence Recognition Using Hybrid Graph Embedding and Adaptive Convolutional Networks
### Significance  
This study seeks to create reliable and flexible systems for recognizing sign language to close the gap in assistive communication technology [13]. This method achieves linguistic equity by acknowledging sign language as a multi-aceted communication system. Advanced sign language recognition systems have far-reaching social and technological consequences, and this study offers a thorough theoretical framework that highlights both. For the deaf community, it seeks to build truly inclusive communication platforms.  
The progression learning convolution neural model demonstrated significant improvements in the credit of sign language gestures using data from wearable glove devices, showcasing its potential for sentence-level translation [14]. The development of a sensor data fusion framework combined with an optimized Elman neural model effectively enhanced the robustness and accuracy of sign language recognition systems [15].  
The study provides an innovative approach to sign language recognition using HGE-ACN, which stands for Hybrid Graph Embedding and Adaptive Convolutional Networks. The process includes adaptable convolutional frameworks, multimodal feature extraction, graph-based embedding methods, and extensive validation on several standard sign language datasets. This approach aims to improve sign language recognition and capture complicated spatial-temporal interactions. The key objective points of the study are as follows:  
To develop an AI-based framework for glove-based sign language sentence recognition using HGE-ACN to process data from wearable glove devices, focusing on six-axis inertial sensors and five-finger curvature sensors and eliminating sensor-based-based inputs.  
To optimize sentence-level recognition using graph-based embeddings and adaptive convolution to solve difficulties including signing speed, signer styles, and hand gesture transitions.  
To build a scalable, real-time recognition system for educational tools, accessibility technologies, and human-computer interaction with multilingual and real-world deployment capabilities to promote inclusive communication systems.  
An overview of the research follows. Section 2 describes a comprehensive literature and research methodology review. Section 3 describes the research plan, methodologies, and processing. Section 4 describes the results of the analysis. The conclusion and future work are in Section 5.
## 2 3D Image Acquisition  
Firstly, 3D ultrasonic images of the human hand used for extracting vascular patterns were collected in a previous work to implement a biometric system based on 3D palmprints [29]. The setup was composed of a commercial ultrasound probe with a frequency of 12 MHz (LA435 by ESAOTE) connected to a CNC pantograph and driven by the ultrasound research scanner ULAOP [30]. The acquisition procedure involves the user dipping his hand in a basin of water with the palm facing upwards while the probe, also immersed in water, is mechanically moved in the direction from the wrist to the end of the palm (fingers excluded), while the system continuously acquires B-mode images. Subsequently, in a post-process phase, they are grouped in a volumetric image of 25 \(\times\) 38 \(\times\) 11 mm\({}^{3}\). This 3D image is described by a 3D matrix V(X,Y,Z) of 542 \(\times\) 814 \(\times\) 238 voxels. The brightness of each voxel is represented in an 8-bit grayscale, where black indicates the complete absence of wave reflection (value 0), while white corresponds to a full-wave reflection (value 255). Figure 1 shows a 3D rendering of a collected sample. In addition to palmprint, other features can be extracted from the same volume for biometric recognition purposes, including vascular patterns, which can be achieved by detecting \"dark\" regions in a B-mode image similar to the one highlighted with red circles in Figure 1.  
Figure 1: Example of a volumetric image of a human hand region; the red circle indicates a vein.
###### Abstract  
Background: This study compared the marginal gap (MG) and absolute marginal discrepancy (AMD) of computer-aided design and computer-aided manufacturing (CAD-CAM) used in open systems (OSs) and closed systems (CSs) for producing monolithic zirconia-reinforced lithium silicate (ZLS) ceramic crowns. Methods: 60 ZLS ceramic crowns were cemented to abutment acrylic teeth; thirty crowns were designed and milled by an OS, and thirty by a CS. All crowns were sectioned for evaluating the marginal gap by scanning electronic microscopy (SEM). To compare the marginal gap between CS and OS techniques, data were analyzed using the independent-samples Mann-Whitney U Test (\(\alpha\) = 0.05). Results: AMD was found to be significantly better for the closed system (\(p\) < 0.05). Mean AMD values for the CS were 148 \(\upmu\)m, and for the OS it was 196 \(\upmu\)m. MG was found to be significantly better for the OS (\(p\) < 0.05). Mean MG values for the CS were 55 \(\upmu\)m, and for the OS they were 38 \(\upmu\)m. Conclusions: The marginal gap in relation to AMD was significantly better for CS. However, the marginal gap in relation to MG was significantly better for OS. Both techniques showed clinically acceptable MG values (<120 \(\upmu\)m).  
CAD-CAM; ZLS; marginal fit; CEREC; marginal gap; open system; closed system ] [[]] [[]] [[]] [[]] [[]] [[]] [[]] [[]] [[]] [[]] [[]] [[]] [[]] [[]] [[] []  
CAD-CAM can be used by two different types of systems. The first is a closed system in which the dentist scans intra-orally, designs, and mills the restoration chair-side with one company flow chart. The second is an open system in which the dentist can scan with any intra-oral scanner, then send the STL file to a laboratory where it is designed and milled [10,11]. Studies on the marginal fit of closed systems have reported acceptable marginal discrepancies of 81 \(\upmu\)m and 87 \(\upmu\)m [12,13]. For open systems, studies have shown marginal discrepancies between 51 \(\upmu\)m and 90 \(\upmu\)m [14,15].  
In the literature, there are many methods which describe how to measure marginal fit. [PERSON] defined the marginal gap (MG) as a vertical line between the restoration to the preparation margin. AMD is measured horizontally from the margin of the restoration to the preparation. The risk of microleakage increases as the MG is increased. AMD represents under- or over-extension of the restoration, which can lead to an increased risk of plaque accumulation [16].  
For measuring marginal gaps, there are several methods such as micro-CT, sectional technique, and silicone paste [8,17,18,19,20]. For evaluating the marginal gap (MG and AMD) we used the sectional method.  
To the best of our knowledge, there are no studies which have compared the effects of closed systems versus open systems on the marginal discrepancy of ZLS crowns. The purpose of this in vitro study was to compare OS versus CS by evaluating the marginal discrepancy of ZLS crowns. The null hypothesis was that no difference would be found between OS and CS techniques.
## 2 Taxonomic and Technical Analysis
### Technological Profile of the Survey  
As stated in the previous section, there is no developed methodology to uniformly divulgate the development of applications based on V2X, I2X, or P2X communications. Nevertheless, it is possible to elaborate a set of basic features that they must fulfill. In order to assess one of the contributions made by this work to the field of V2X, I2X, and P2X communications, a comparison of the characteristics and capabilities including a set of 100 representative manuscripts collected in this review is shown in Table A1 (Appendix A). The followed methodology uses the number of citations as criteria to select the most representative papers based on the guidelines to make a literature review mentioned in [19]. Although the criteria could cause a potential bias in the results, since the number of citations increases with time and, thus, the selection could disqualify the most recent papers in the area, we have obtained the following distribution: 20.79% of the documents were published between 2000 and 2009, 45.54% of the documents were published in the period 2010-2013, and 33.67% were published between 2014 and 2018. This means that one third of the papers were published in the last five years, giving a significant insight on the current state-of-the-art.  
The analysis about the current research shows that the most productive country is the US (29.7%), with the study of analytical models for vehicular communications (11.88%) and road safety applications (7.92%) being its main contributions (Figure 3a). The next largest contributor is Spain (13.86%), which is mainly focused on road safety applications (6.93%), followed by China (11.88%) in the study of analytical models (8.91%). On the other hand, 2011, 2013, and 2015 were the most representative years (14.85%, 12.87%, and 11.88%, respectively) with contributions in analytical models (15.84%), road safety applications (9.9%), and cybersecurity (3.96%). Moreover, the results show that V2X is the type of communication most investigated (92.14%), with 2011, 2013, and 2015 being the most productive years (14.71%, 12.75%, and 10.78%, respectively) (Figure 3b). I2X is the second type of communication most studied (4.9%), focusing its contributions in 2004, 2010, 2015, and 2016 (0.98%, 1.96%, 0.98%, and 0.98%, respectively). Finally, P2X has barely been studied, only becoming a field of study in recent years (1.96%) where 2016 and 2017 were the main years of contribution (0.98% and 0.98%, respectively).
## 3 Results and Discussion  
### Extraction, Color, and Yield of C. japonica Leaf EO  
1.1 Effects of the Distillation Method on the Color and Yield of the Investigated Leaf EO Samples (EO-HD and EO-WSD)  
Table 1 shows the color and yield of the EO-HD and EO-WSD samples. The yield results were expressed as the mean \(\pm\) standard deviation of three replicates. Comparing the leaf EO yield (%, _v/w_), on a fresh weight (FW) basis, a significantly higher value was obtained by HD (1.21 \(\pm\) 0.03%) than that produced by WSD (0.45 \(\pm\) 0.01%). Concerning the EO color, the EO-HD sample showed a pale-yellowish, while the EO-WSD sample was colorless. Based on the results obtained in this study, it was proven that the distillation method significantly affects the _C. japonica_ leaf EO color and yield, with HD being the most efficient EO extraction method compared with the WSD. The higher EO yield in the HD method, using a Clevenger-type apparatus, may be due to the conditions prevailed during distillation such as the recycling of hydrosol that allowed it to recover the dissolved EO, since it can be re-boiled.  
The same yield pattern was also observed in similar studies with other aromatic plants, such as the case of rose-scented geranium (_Pelargonium_ sp.) EO, which presented yield values of 0.16-0.22% and 0.09-0.12% for EO-HD and EO-WSD, respectively [9].
# IoT and UAV Integration in 5G Hybrid Terrestrial-Satellite Networks
## 2 IoT Technologies
\begin{table}
\begin{tabular}{l l l l l l l l l} \hline \hline
**Standardization Bodies and Special Interest Groups** & **Name** & **Modulation** & **Band** & **MAC** & **Data Rate** & **Coverage** & **Number of Channels** \\ \hline \multirow{3}{*}{IEEE} & 802.15.4 k & DSS8, PSK & ISM SUB-GHz and 2.4 & CSMA/CA, & \multirow{3}{*}{1.5–128 kbps} & 5 km (urban) & multiple (depends on \\  & & & GHz & & & & & channel and modulation) \\ \cline{2-7}  & 802.15.4 g & PSK, OFDMA, & ISM SUB-GHz and 2.4 & \multirow{3}{*}{CSMA/CA} & \multirow{3}{*}{4.8-800 kbps} & up to several & multiple (depends on \\  & & COPSK & GHz & & & & & channel and modulation) \\ \hline \multirow{3}{*}{Weightless-SIG} & -W & 16 QAM, DBPSK & TV white spaces & \multirow{3}{*}{TDMA/FDMA} & 1 kbps-10 & 5 km (urban) & 16 or 24 \\  & & (470-790 MHz) & & & & & \\ \cline{2-7}  & -N & DBPSK & ISM SUB-GHz & \multirow{3}{*}{ALOHA} & 30-100 kbps & up to 3 km & multiple, 20 Hz each \\ \cline{2-7}  & -P & GPSK, QPSK & ISM SUB-GHz or & \multirow{3}{*}{TDMA/FDMA} & 200 bps-100 & up to 2 km & multiple, 12.5 kHz each \\  & & & licensed & & & & (urban) \\ \hline DASHAlliance & DASH7 & GPSK & SUB-GHz & CSMA/CA & 9,65.56 & up to 5 km & multiple, 25 or 200 kHz \\  & & & & & & & \\  & & & & & & & \\  & & & & & & & \\  & & & & & & & \\  & & & & & & & \\  & & & & & & & \\  & & & & & & & \\ \hline \multirow{3}{*}{3 CPP} & \multirow{3}{*}{NB-IoT} & \multirow{3}{*}{NB-IoT} & QPSK, 16 QAM, & \multirow{3}{*}{Licensed LTE} & SC-FDMA (UL) & \multirow{3}{*}{35 km} & multiple, 180 & \\  & & & & & & 20 kbps (UL) & & kHz each \\ \cline{2-7}  & & & & & & & \\  & & & & & & & \\  & & & & & & & \\  & & & & & & & \\ \hline \multirow{3}{*}{ETSI} & \multirow{3}{*}{LTN} & BPSK (UL) & BM SUB-GHz & BPSK (UL) & \multirow{3}{*}{10-100 bps} & \multirow{3}{*}{up to 60 km} & \multirow{3}{*}{multiple, 200 Hz each} \\  & & GPSK (UL) or CSS8 & (433, 868 and 915 MHz) & GPSK (DL) & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Technical specifications for standardization bodies and special interest groups for IoT solutions [8]. ETSI, European Telecommunications Standards Institute.
## 4 Experimental Results and Discussion
### Vision Capabilities Simulations
#### 4.2.2 Recognition in Open-Space Controlled Environment  
Figure 7 reports some of the network's inference results using some photographs taken in an open-space, yet controlled test environment, where grape cluster replicas have been hung next to real foliage.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **416@VOC+** & **416@VOC+** & **608@VOC+** \\  & **WGISD** & **WA-WGISD** & **WA-WGISD** \\ \hline
**\# detections** & 58,313 & 94,062 & 88,623 \\
**\# unique truths** & 12,882 & 12,979 & 12,979 \\ \hline
**Precision@.25** & 0.72 & 0.67 & 0.70 \\
**Recall@.25** & 0.72 & 0.75 & 0.78 \\
**F1-score** & 0.72 & 0.71 & 0.74 \\
**TP@.25** & 9237 & 978 & 10,147 \\
**FP@.25** & 3530 & 4768 & 4386 \\
**FN@.25** & 3645 & 3191 & 2832 \\
**avg IoU** & 0.55 & 0.52 & 0.53 \\
**mAP@.5** & 0.72 & 0.75 & 0.77 \\
**AP@.5** (**\(\boldsymbol{\text{`}}\)grapes\(\boldsymbol{\text{`}}\)class)** & 0.50 & 0.66 & 0.80 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Neural network performance summary for grape recognition.  
Figure 6: Grape cluster replicas used during simulations.  
The bounding boxes shown on each image (labeled with \"grapes\") were inserted synthetically by the script that applies the inference result and represents the position returned by the network for each recognized cluster replica, with the related confidence percentage.
## 2 Methods
### Procedure  
Participants were tested in a quiet room in our clinical setting. Infant's gaze during the task was recorded with an SMI Eye-Tracking device provided by SensoMotoric Instruments (Teltow, Germany), with a sample rate of 500 Hz and accuracy better than 31 degrees of visual angle. No explicit instruction was given to the infant. The eye-tracking device was positioned in front of each participant and below a 22-inch flat-screen monitor where the stimuli were presented. The distance of the infants from the screen was approximately 50 cm, and the inclination angle of the eye-tracking device was adjusted for each infant. Infants were seated on their parents' lap: Parents were instructed to wear dark sunglasses to prevent the eye-tracking system from capturing their gaze. Additionally, they were asked not to intervene during the eye-tracking procedure and to hold the child to enhance his/her comfort. Before the stimuli were presented, a 5-point calibration procedure was conducted. According to the literature on using eye-tracking procedures with very young subjects [48], this calibration balances a good result with minimal perceived intrusiveness for the user.  
Following this, each child passively viewed the experimental stimuli. The calibration was repeated until the deviation from the known calibration target for both the x and y components was below 2\({}^{\circ}\). The accuracy of calibration between the two groups was calculated as the root mean square value of the deviation of the x and y components. The root mean square was not significantly different between the two groups. A total of 9 subjects (IL = 4, RL = 5) were excluded due to invalid calibration.  
The experimental paradigm included 4 blocks of 6 trials each for a total of 24 trials (7 min in total), presented in random order. Experimental stimuli were interspersed with filler stimuli (animations or cartoon scenes) to reorient infant attention towards the screen and to avoid fuzziness.
###### Abstract  
There has emerged substantial research in addressing single-view 3D reconstruction and the majority of the state-of-the-art implicit methods employ CNNs as the backbone network. On the other hand, transformers have shown remarkable performance in many vision tasks. However, it is still unknown whether transformers are suitable for single-view implicit 3D reconstruction. In this paper, we propose the first end-to-end single-view 3D reconstruction network based on the Pyramid Vision Transformer (PVT), called ED\({}^{2}\)IF\({}^{2}\)-Net, which disentangles the reconstruction of an implicit field into the reconstruction of topological structures and the recovery of surface details to achieve high-fidelity shape reconstruction. ED\({}^{2}\)IF\({}^{2}\)-Net uses a Pyramid Vision Transformer encoder to extract multi-scale hierarchical local features and a global vector of the input single image, which are fed into three separate decoders. A coarse shape decoder reconstructs a coarse implicit field based on the global vector, a deformation decoder iteratively refines the coarse implicit field using the pixel-aligned local features to obtain a deformed implicit field through multiple implicit field deformation blocks (IFDBs), and a surface detail decoder predicts an enhanced displacement field using the local features with hybrid attention modules (HAMs). The final output is a fusion of the deformed implicit field and the enhanced displacement field, with four loss terms applied to reconstruct the coarse implicit field, structure details through a novel deformation loss, overall shape after fusion, and surface details via a Laplacian loss. The quantitative results obtained from the ShapeNet dataset validate the exceptional performance of ED\({}^{2}\)IF\({}^{2}\)-Net. Notably, ED\({}^{2}\)IF\({}^{2}\)-Net-L stands out as the top-performing variant, exhibiting the highest mean IoU, CD, EMD, ECD-3D, and ECD-2D scores, reaching impressive values of 61.1, 7.26, 2.51, 6.08, and 1.84, respectively. The extensive experimental evaluations consistently demonstrate the state-of-the-art capabilities of ED\({}^{2}\)IF\({}^{2}\)-Net in terms of reconstructing topological structures and recovering surface details, all while maintaining competitive inference time.  
3D reconstruction; single-view; deep learning; computer vision; transformer; implicit field; signed distance function; displacement field +
Footnote †: journal: Applied Sciences