esa-sceva/satcom-chunk-collection huggingface dataset:
# Real-Time Image Detection for Edge Devices: A Peak Fruit Detection Application
## 2 Materials and Methods
### SSD: Single Shot Detector  
A single-shot detector (SSD) [37] is n state-of-the-art object detection model that outperforms its competitors, \"you only look once\" (YOLO) [38] and faster R-CNN [39], in terms of accuracy and inference time [37]. Therefore, the SSD model was used as a  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Split** & **Cultivar** & **Images** & **Fruits (Labels)** \\ \hline \multirow{4}{*}{Train} & Sweet Dream & 270 & 2015 \\ \cline{2-4}  & Royal Time & 248 & 1066 \\ \cline{2-4}  & Catherine & 305 & 4564 \\ \hline \multirow{4}{*}{Test} & Sweet Dream & 66 & 453 \\ \cline{2-4}  & Royal Time & 63 & 270 \\ \cline{2-4}  & Catherine & 76 & 1480 \\ \hline \multicolumn{4}{c}{Total of training} & 823 & 7645 \\ \hline \multicolumn{4}{c}{Total of testing} & 205 & 2203 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Statistics of the dataset.  
Figure 1: Hardware platform (edge device) for performing inferences.  
detector in this study. Similarly to any model for computer vision tasks that is based on deep learning, the first step of SSD is feature extraction. This block is a convolutional neural network (CNN) and is usually referred to as the backbone of the model. The output of the backbone is a feature map containing the relevant information required to solve computer vision tasks. It is important to emphasize that the variations in the SSD model are on the backbone when selecting the CNN and performing optimizations (as described in Section 2.4). The remainder of the SSD model is constructed by adding additional layers of functionality at the end of the backbone. The SSD model partitions specific feature maps into standard boxes and generates scores for the presence of objects in each box. Additional technical details of the SSD model can be found in [37].  
As mentioned previously, SSD variations were performed on the backbones. In this study, experiments were conducted using a MobileNet CNN as the backbone for the SSD model to investigate the trade-off between the detection accuracy and inference time. The backbones used were MobileNetV1, MobileNetV2, MobileNet EdgeTPU, and MobileDet.
# Novel Methodology for Condition Monitoring of
## 3 Methodology  
In this section, we describe the proposed diagnosis methodology applied to the identification and classification of different levels of uniform wear in gears as an incipient failure in a gearbox transmission system. The methodology is mainly composed of six steps, as shown in Figure 1. In the first step, different conditions of uniform wear in gears are experimentally evaluated under continuous working conditions in a gearbox transmission system. Then, the second step considersthe continuous monitoring of the different conditions under evaluation, and from the continuous monitoring, infrared images are acquired, which contain meaningful information related to the current condition under assessment. Afterward, in the third step, the detection of hot spots over the infrared image is identified, aiming to compute its corresponding thermal matrix. Subsequently, in the fourth step, for each evaluated condition, a significative statistical set of features is estimated from the previous thermal matrices. Then, in the fifth step, a dimensionality reduction is carried out by means of linear discriminant analysis, aiming to obtain a visual representation of all considered conditions in a 2D space. Additionally, with the consideration of the dimensionality reduction stage, the classification task is facilitated for the considered classification algorithm. Finally, in the last step, automatic fault identification is performed by means of an artificial neural network; thus, the condition assessment results in the classification of the different levels of uniform wear in the gears of the gearbox transmission system.
## 2 Data and Methods  
### Study Area  
HYNPP is situated in Haiyang City, China, at the eastern tip of a cap surrounded by the sea. To the northeast lies Rushan Bay, to the southwest is Haiyang Harbor, and to the east and south is the vast Yellow Sea of the Western Pacific Ocean, as depicted in Figure 1a,b. The site is located within a region classified as having a temperate continental monsoon climate. Distinct seasonal variations in wind direction are observed, with prevailing northwesterly winds in winter and predominantly southeasterly winds in summer. The annual mean wind speed, temperature, and water vapor pressure are 3.3 m/s, 11.9 \({}^{\circ}\)C, and 12.2 hPa, respectively. The sea area around HYNPP is primarily influenced by regular semi-diurnal tidal currents, with a multi-year average tidal range of approximately 2.39 m. The tidal currents are predominantly reciprocating, flowing mainly within a range of WSW-WNW during flood tide and within a range of ENE-ESE during ebb tide. The ebb tide is generally stronger than the flood tide.  
Designed to house six pressurized water reactor (PWR) nuclear power units, HYNPP currently operates units 1 and 2, commissioned in January 2019 (Table 1). As shown in Figure 1c, the plant's cooling water is sourced from an open channel intake on the south side. The Phase I discharge outlet is located on the west side, while the inactive Phase II outlet is on the east side.  
A 40.4 km\({}^{2}\) sea area surrounding HYNPP is designated as a Type III EFZ. This zone encompasses two main areas: the thermal mixing zone and the external nuclear power utilization zone. The thermal mixing zone is divided into two sub-areas: Zone A (9.6 km\({}^{2}\)) on the west side and Zone B (7.7 km\({}^{2}\)) on the east side of the plant site, as shown in Figure 1c. Zone C (23.1 km\({}^{2}\)), located on the south side, is designated as the external nuclear power utilization zone, which is also a Type III EFZ. Generally, the main water quality indicators in HYNPP's EFZs adhere to Type II sea water quality standards [32], with the exception of water temperature.
# FTIR Spectroscopy for Identification and Intra-Species Characterization of _Serpula Iacrymans_
## 2 Materials and Methods
### FTIR Analysis  
The FTIR analysis was carried out using an ALPHA II spectrometer equipped with an ATR Platinum crystal diamond module (Bruker Optics, Marne la Vallee, France). The analysis was performed between 4000 and 400 cm\({}^{-1}\) with 64 accumulations per sample and a spectral resolution of 2 cm\({}^{-1}\). The infrared data were cleaned and processed with OPUS (version 6.5, Marne la Vallee, France) software as shown below. The spectrum was first trimmed by removing the values on either side of the interval between 3700 and 600 cm\({}^{-1}\), and then normalised. Normalisation of the spectra was conducted according to two models: a minimum-maximum (min-max) normalisation and a vector normalisation. For the min-max normalisation of the spectra, the data were smoothed (25 smoothing points), a straight line was generated between 2800 and 1800 cm\({}^{-1}\) and the baseline was corrected before normalisation. All values of the spectra were set between 0 and 2, while keeping the distances between them. For the vector normalisation of the spectra, the data were second-derivative smoothed (25 smoothing points), a straight line was generated between 2800 and 1800 cm\({}^{-1}\), and then a vector normalisation was performed. The second derivative allowed for better resolution of the near and small absorption peaks. This was completed by calculating the average absorbance values of each spectrum in the selected spectral range. Each absorbance value in the spectrum was subtracted from this average value. The corrected value was then divided by the root of the sum of the squares of all the absorbances in the spectral range.
# Investigation on Reference Frames and Time Systems in Multi-GNSS
## 3 Data Used and Adopted Model of the Pseudorange
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Symbol** & **Unit** & **Meaning** \\ \hline c & m/s & Speed of light \\ \hline t & s & Time of reception in the receiver time scale \\ \hline t\({}^{\prime}\) & s & Time of transmission in the specific GNSS time scale: \(t^{\prime}\) = \(t-p(t)/c\) \\ \hline t\({}_{\mathrm{of}}\) & s & Time of flight \\ \hline LS & s & Position of satellites must be computed at time \(t-t_{\mathit{of}}-LS\), where LS = full \\  & & leap seconds for GLONASS; 14 s for BeiDou; 0 s for other GNSS \\ \hline \multirow{2}{*}{dT\({}_{\mathrm{Rel}}\)} & \multirow{2}{*}{s} & Periodic part of relativistic correction (which is not considered for \\  & & GLONASS since it is already included in the polynomial correction [3]) \\ \hline \(\omega_{\mathrm{e}}\) & rad/s & Earth rotation rate appropriate for the GNSS constellation \\ \hline X, Y, Z & m & Earth-Centered Earth-Fixed (ECEF) coordinates of satellite \\ \hline x, y, z & m & ECEF coordinates of receiver \\ \hline TSC\({}_{\mathrm{X}}\) & s & Time System Correction of the X \({}^{1}\) GNSS relative to a common time scale \\ \hline dT\({}_{\mathrm{Rec}}\) & s & Receiver clock offset relative to a common time scale \\ \hline dt & s & Satellite Clock offset relative to a specific GNSS time scale \\ \hline TZD & m & Tropospheric Zenith Delay \\ \hline El & \({}^{\circ}\) & Elevation \\ \hline \end{tabular} \({}^{1}\) G: GPS; R: GLONASS; E: Galileo; C: BeiDou; J: QZSS; I: NAVIC (formerly IRNSS); N: GAGAN.  
\end{table} 
Table 7: Explanation of symbols and variables used in Equations (2)–(6).
## 3 Diverse Applications of EEG, Machine Learning, and Deep Learning in Rehabilitation  
Embarking on an exploration of neural rehabilitation, the research delves into the multifaceted activities that can be addressed through the mutual application of EEG, machine learning, and deep learning [13; 14; 19; 31; 32; 33]. These innovative interventions bring about transformative possibilities across various neural rehabilitation domains.  
1. Motor Rehabilitation: The utilization of machine learning algorithms in decoding EEG signals during motor imagery tasks opens avenues for controlling external devices, including robotic exoskeletons and prosthetics [13; 19; 31; 33]. Systems providing feedback in real time not only instruct users through precise motor tasks but also contribute significantly to motor skill relearning and the promotion of neuroplasticity [14; 19; 32; 33].
2. Cognitive Rehabilitation: EEG signals play a pivotal role in gauging and enhancing attention stages using Neurofeedback techniques [8; 11; 13; 34]. The adaptability of machine learning facilitates the tailoring of training procedures tailored to individual mental states, while the nuanced capabilities of deep learning contribute to the design of personalized memory training tasks [7; 8; 13; 34].
3. Neuropsychiatric Rehabilitation: EEG neuroimaging emerges as a beneficial instrument for handling stress and anxiety, utilizing artificial intelligence to recognize patterns associated with stress and triggering interventions for relaxation [10; 28; 35; 36].  
Moreover, neuroimaging based on EEG assists those with ADHD in refining concentration and controlling attention by reinforcing preferred brain activity patterns [13].
* Walking Rehabilitation: Integrating ML and EEG with systems that capture movements allows for a comprehensive analysis of gait patterns, providing real-time feedback during walking exercises and potentially revolutionizing rehabilitation approaches [13; 31; 34; 37].
* Visual and Hearing Restoration: Protocols relying on EEG, in collaboration with advanced machine learning approaches, provide an intricate structure for creating customized training activities for individuals experiencing sensory perception challenges in both vision and hearing [32; 38].
* Multimodal Rehabilitation: The integration of EEG with other cutting-edge technologies, such as virtual reality or functional near-infrared spectroscopy, paves the way for innovative multimodal rehabilitation approaches [19; 20; 33; 39; 40].  
The inherent flexibility of these technologies holds significant promise for not only enhancing rehabilitation outcomes but also substantially improving the overall quality of life for individuals grappling with neurological disorders.
# Advances in the Biosynthesis and Molecular Evolution of Steroidal Saponins in Plants
## 5 Distribution of Steroidal Saponins in Plants
### Liliales Plants  
Liliales is an early diverged lineage of monocots, and the plants in the lineage containing steroidal saponins are the Liliaceae, Melanthiaceae, and Smilaceae.  
1. Liliaceae plants. At present, the research on steroidal saponins of Liliaceae plants has mainly focused on _Lilium_ and _Frillaria_ plants [103]. More than 80 steroidal saponins have been isolated and identified from the bulbs of _Lilium lancifolium, \(L\). _pumilum_, \(L\). _longiflorum_, \(L\). _candidium_, \(L\). _specisum_, \(L\). _tenuifolium_, \(L\). _callosum_, and other _Lilium_ plants, which are mainly divided into four types: spirostandol-type, furostandol-type, isospiorstandol-type, and pseudospiorstandol saponins [103]. Specifically, spirostandol saponins isolated from _Lilium_ plants mainly include lilioglycoside B/H and brownio-side, which are unique to _Lilium_ plants [103; 104; 105; 106; 107]; the isolated isospiorstandol saponins include lilioglycoside C, D, and I, respectively; and furostandol saponins mainly include lilioglycoside K, N, R and pardarinoside A-D, F, and G [103; 104; 105; 107]. Among _Frillaria_ plants, only 12 steroidal saponins have been isolated and identified from the bulbs of _Fritillaria pallidiflora_, such as pallidiflosides D/E/G/H/I, protobioside, and Polyphyllin V [108] (Supplementary Table S1).
2. Melanthiaceae plants. Among the plants in this family, _Paris_ and _Trillium_ are the largest two genera; they are important Chinese medicine plants with a long medicinal history [109; 110]. A variety of steroidal saponins have been isolated and identified from the rhizomes of Paris plants, including spirostandol-type, furostandol-type,cholestane-type, pregnane-type, and polyhydroxylated-type saponins [8]. Steroidal saponins isolated and identified from Trillium plants can be divided into the following types: spirostand, furostanol, and pennogenin saponins. Among them, the specific saponins of Trillium plants are trillins, trikamsteroside C, trillenoside A, parisapioside C, etc. [111] (Supplementary Table S1).
3. Smilaceae plants. The most common plants containing steroidal saponins are _Smilax_ plants [7]. A total of 104 steroidal saponins have been isolated from about 20 species of _Smilax_ plants [7]. These steroidal saponins include five types: spirostand-type, isospiorstand-type, furostanol-type, pregnane-type, and cholestane-type; most of the linked sugar groups are monosaccharides or disaccharides [7; 112; 113; 114; 115]. Steroidal saponins are also found in a few monocots, such as Poales and Zingiberales. Poaceae are only _Avena_ plants (oats), such as _Avena sativa_ and _A. striposa_. A total of 16 steroidal saponins were isolated and identified from oats, including Avenacoside A, B, C, D, etc. [116]. Oats are also one of the few plants that contain both triterpenoid saponins and steroidal saponins. However, _Costus Spiralis_ and _C. speciosus_ are the only Zingiberales containing steroidal saponins in their rhizomes [117; 118] (Supplementary Table S1).
## 3 Nanoparticles in EOR
### Titanium Oxide  
The potential use of titanium oxide (TiO\({}_{2}\)) NPs for EOR has been proposed by several researchers. [PERSON] et al. [130] employed TiO\({}_{2}\) NPs in water to enhance oil recovery in a sandstone core plug and discovered that TiO\({}_{2}\) NPs could improve recovery from 49% to 80% by altering the wettability due to uniform adsorption, as proved by an SEM study. Similar experiments with lower concentrations of TiO\({}_{2}\) NPs (0.01 and 0.05 wt %) [85] revealed that TiO\({}_{2}\) did not affect viscosity and interfacial tension but did change the wettability from oil-wet to water-wet. [PERSON] et al. [87] studied the effect of Al\({}_{2}\)O\({}_{3}\) and TiO\({}_{2}\) on the Berea sandstone and showed that a combination of TiO\({}_{2}\) NPs with PVP as the dispersant yielded the highest recovery compared with Al\({}_{2}\)O\({}_{3}\), SiO\({}_{2}\), brine-only, and dispersant-only. They concluded that TiO\({}_{2}\) NP is the most effective NP for EOR compared to the other metal oxide NPs, with wettability alteration as the dominant mechanism [2].  
The possibility of combining TiO\({}_{2}\) NPs with surfactants or polymers in chemical EOR has also been investigated. [PERSON] et al. [91] showed that oil recovery increased by 4.85% when using 2.2 wt % TiO\({}_{2}\) in surfactant solution compared to pure surfactant. Similarly, by combining a polymer with 2.3 wt % TiO\({}_{2}\), the recovery of oil was improved by 3.9% compared to conventional polymer flooding due to the viscosity improvement of the displacing fluid [131]. Moreover, [PERSON] et al. [132] observed an effect of TiO\({}_{2}\) on polymer-surfactant flooding which showed positive recovery improvement compared to conventional polymer-surfactant flooding.
# Role of Forster Resonance Energy Transfer in Graphene-Based Nanomaterials for Sensing
## 2 Origin of Fluorescence in Graphene-Based Nanomaterials and the FRET Process  
The transitions of electrons from various excited states to the ground state are responsible for GO fluorescence. Each band of functionalized GO appears as a result of particular electronic transitions between the antibonding and non-bonding (\(\alpha^{\bullet}\)\(\rightarrow\)n and \(\pi^{\bullet}\)\(\rightarrow\)n) and bonding (\(\pi^{\bullet}\)\(\rightarrow\)\(\pi\)) orbitals (Figure 1) [30,31]. For instance, GO composed of carboxyl-, hydroxyl-, carbonyl-, and epoxy-conjugated rings, thus providing multiple bands in the fluorescence for various electronic transitions. These multiple bands could overlap with each other to produce a broad signal in the emission wavelength.  
The introduction of various functionalities, strains, localized domains, lateral sizes, and dopants with GO could tune the energy levels, which leads to significant changes in the shape and width of the signals, as well as peak shifts in the longer wavelength region [32,33]. For instance, when GO is treated with aq. KOH or HNO\({}_{3}\), the number of -OH or -COOH functionalities, respectively, increases, which leads to considerable changes in the absorbance and emission wavelengths (Figure 1). Notably, the GO bandgap is generated by the introduction of various functional groups, resulting in sp\({}^{2}\) and sp\({}^{3}\) hybridized carbon atoms. In addition, tuning the ratio of sp\({}^{2}\) and sp\({}^{3}\) hybridized carbon shifts the visible fluorescence to the near-infrared (NIR) wavelength [34]. Similar to GO, GQDs also have the same fluorescence properties via the doping process, which modulates the nature of the electronic properties and fluorescence. For instance, when increasing the doping of N in GQDs, the emission wavelength was shifted from green to red, and to NIR via a quantum confinement effect, i.e., \(\pi\) electrons retained in localized sp\({}^{2}\) domains [35,36].
# A Non-WSSUS Channel Simulator for V2X Communication Systems
## Abbreviations  
The following abbreviations are used in this manuscript:  
3 GPP - Third-Generation Partnership Project  
5G - fifth-generation  
AOA - angle of arrival  
AOD - angle of departure  
AWGN - additive white Gaussian noise  
BER - bit error rate  
CIR - channel impulse response  
C-V2X - cellular-based V2X  
DMRS - demodulation reference signal  
DSRC - dedicated short-range communications  
D2D - device-to-device  
F2M - fixed-to-mobile  
GI - guard interval  
IEEE - Institute of Electrical and Electronics Engineers  
i.i.d. - independent and identically distributed  
IO - interfering object  
ITS - intelligent transportation systems  
LS - least squares  
LTE - Long-Term Evolution  
LTE-V2X - LTE-based V2X  
LTS - long training symbols  
MSs - mobile stations  
OFDM - orthogonal frequency division multiplexing  
RB - resource blocks  
SC-FDMA - single carrier frequency division multiple access  
SOC - sum-of-cisoids  
STA - spectral temporal averaging  
STS - short training symbols  
TF - time and frequency  
V2X - vehicle-to-everything  
V2I - vehicle-to-infrastructure  
V2N - vehicle-to-network  
V2P - vehicle-to-pedestrian  
V2V - vehicle-to-vehicle  
WLAN - wireless local area network  
WSS - wide sense stationary  
WSSUS - wide sense stationary uncorrelated scattering
# Generalized Thrust Network Analysis of Triangular Masonry Cross Vaults Inspired by [PERSON]
## 5 Parametric Analyses of Triangular Parabolic Masonry Cross Vaults  
The Musmeci vault is here regarded as an instance of a class of triangular parabolic masonry cross vaults whose geometry is described by the rise-to-span ratio \(f/2a\) and the uniform normalized thickness \(h/2a\). The structural safety of such a class of vaults is thus investigated, accounting for their parameterized geometry.  
As proposed in reference [17], the minimum and maximum thrusts are explored, resulting from outward or inward horizontal settlements in the rib planes at the corner supports, respectively. In Figure 8, the normalized thrust values, \(H_{\text{min}}/V\) and \(H_{\text{max}}/V\) are reported versus the normalized thickness \(h/2a\), for the selected values \(f/2a=\{0.2,\,0.3,\,0.5,\,0.7,\,1\}\) of the rise-to-span ratio. Red, yellow, and blue curves correspond to orthogonal (\(\lambda=0\)), fan-like (\(\lambda=1\)), and optimized (\(\lambda=\lambda_{\text{opt}}\)) beam networks, respectively. As expected, an increase in \(f/2a\) implies a general decrease in the thrust regime, with the optimized network shape parameter shifting from \(\lambda=0\) to \(\lambda=1\) while considering vaults that are more and more slender. Moreover, an increase in \(h/2a\) implies a larger interval \([H_{\text{min}},H_{\text{max}}]\) of admissible thrusts for the vault, to be interpreted as an increase in the vault robustness.  
A parametric analysis of the minimum thickness of the vault, with respect to the rise-to-span ratio \(f/2a\), is then performed. The relevant results are shown in Figure 9, where the estimates obtained using the orthogonal, fan-like, and optimized networks of beams are compared. It is observed that the optimized minimum thickness versus rise-to-span ratio curve is characterized by two branches. The first [resp., second] branch, corresponding to shallow [resp., slender] vaults, is increasing [resp., decreasing] and attained with an orthogonal [resp., fan-like] network topology. The transition between the two branches takes place for vaults with intermediate values of \(f/2a\) and requires intermediate values of the network shape parameter \(\lambda\). In particular, a maximum of the curve is observed within such a connection branch, implying that the geometric safety factor of triangular parabolic masonry cross vaults under self-weight improves when considering shallow or slender geometries.  
Figure 8: Parametric analyses of triangular parabolic masonry cross vaults: minimum/maximum normalized thrust \(H/V\) versus normalized thickness \(h/2a\), for vaults with parabolic webs characterized by, from top to bottom, rise-over-span ratio \(f/2a=\{0.2,\,0.3,\,0.5,\,0.7,\,1\}\). Orthogonal (\(\lambda=0\)), fan-like (\(\lambda=1\)), and optimized (\(\lambda=\lambda_{\text{opt}}\)) beam networks are considered.