esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Developed Equipment and Experimental Setup
### FRF Test Using Developed Equipment
#### 3.2.2 FRF Results of Cap Impact  
In Figure 4, only the FRF was analyzed via striking of porcelain. However, in Figure 5, the striking of the cap was performed in an attempt to identify the difference between normal and defect caps in (a) time domain, (b) FFT domain, and (c) FRF domain, where A is normal alumina material, C is normal cristobalite material, and AD and CD are metal damage test pieces of each material. In the time domain, the \(y\)-axis is normalized to the maximum negative peak; it is difficult to distinguish the difference between the normal and the damaged cap except for the fine envelope difference of the time signal. In the FFT domain, a shift in frequency peaks between the normal and the metal damage specimens was confirmed. However, because the manufacturing of porcelain insulators is different, determining the defects only by the frequency shift of FFT may not be a reliable approach. The eigenmode peak difference was observed in the FRF domain, and it was confirmed that the FRF waveform of the damaged specimen was higher than that of the normal specimen in the frequency range of 4-10 kHz, and the magnitude was higher. Analysis of the data in the FRF domain via investigation of the three domains seems to be a suitable method for more accurately identifying differences.  
Figure 4: FRF results from the impact of porcelain. (**a**) FRF results of normal and cap damage; (**b**) FRF result of 5 to 10 kHz at normal C.  
Figure 5: Comparison of time, FFT, and FRF results by the impact of the cap: (**a**) time domain of normal and damaged cap specimens; (**b**) FFT domain of normal and damaged cap specimens; and (**c**) FRF domain of normal and damaged cap specimens.  
As per the FRF results shown in Figure 5, analyzing all the data obtained from 0 to 10 kHz may prove to be an inefficient method for extracting the correct features. Therefore, for efficient analysis, as shown in Figure 6, the frequency range of the FRF graph is divided into five ranges: 0-10 kHz, 0-4 kHz, 4-10 kHz, 4-7 kHz, and 7-10 kHz. Using these divisions, feature extraction was performed to find the optimal frequency range that distinguishes the normal condition from the defect.
## 4 Conclusions  
The results reported here showed that a titanium dental implant surface, permeated with CGF or LPCGF, contained fibrin, which is fundamental to accommodate the cellular network. The permeated dental implant surface was found to slowly release VEGF, a growth factor indispensable in creating intercellular communication and neo-angiogenesis, during bone regeneration and healing [17, 18, 19].  
Figure 5: ELISA quantification of human VEGF into PBS from CGF- and LPCGF-permeated implants at various times (0–3 days). Values are means \(\pm\) SD, n = 3. * \(p\) \(<\) 0.05 denotes statistically significant differences between the VEGF released by CGF- and LPCGF-permeated implants.  
The devices used in this study could be employed to produce the first biologically active implant surface, permeated with both fibrin (which is essential to accommodate the cellular network) and growth factors (which are essential to create intercellular communication and neo-angiogenesis). By using this procedure, the osseointegration process becomes bilateral, operating both from the bone towards the implant, and from the implant towards the bone. This could reduce healing time and potentiate the physiological response.  
It will, thus, become possible to expand the application of this type of surface in other fields of medicine, including orthopedics, maxillofacial surgery, and plastic surgery. Further studies are needed to investigate the use of biologically active surfaces, in greater depth, and to further improve the implant micro-surfaces, making them increasingly permeated by the autologous growth factors.  
Conceptualization, A.P., L.S., M.A.C., and T.B.: methodology, A.G., C.D., F.D., F.F., and E.S.: validation, A.P., F.F., E.S., and L.S.: investigation, A.G., C.D., F.D., F.F., and E.S.: resources, E.S.: writing--original draft preparation, E.S. and F.F.: writing--review and editing, A.P., F.F., E.S., M.A.C., and L.S.: supervision, A.P. and L.S.: project administration, L.S.: funding acquisition, A.P., L.S., and T.B.  
This research was funded by Ministero dello Sviluppo Economico project PON-MISE OSTEO-CARE, Prog. n. F/050370/03/X32.  
The authors state that this article does not engender any conflicts of interest, whatsoever, with any organization or individual.
## 3 Results
### Deep Learning Techniques  
The overview of deep learning techniques is presented in Table 3. In order to leverage the information contained in different retinal images, multi-modal deep learning techniques were proposed. For example, [PERSON] et al. [33] proposed a convolution neutral network (CNN)-based model to classify AD patients using OCT, optical coherence tomography angiography (OCTA), ultra-widefield (UWF) scanning laser ophthalmoscopy (SLO) color, fundus autofluorescence images, and patient medical record data. The network consisted of a five-layer CNN followed by an FC layer. The information from different images was fused by taking the average of output of the network for each image. A total of 1136 images from 159 individuals were used for training and testing the model, achieving an AUC of 83.6% in the test set. In another study, [PERSON] et al. [22] developed an ensemble deep learning method to differentiate between cognitively impaired and healthy individuals. OCT images and fundus photographs centered on the macula and optic disc were used in this work. The information from different images was fused by concatenating feature vectors that were obtained independently by feature encoders. The joined feature vector was fed into a fully connected layer to obtain a classification result. Four different feature encoders were used, such as VGG-19 and ResNet-50, and the final result was the average of these different networks. The training set comprised 2356 subjects, while the test set contained 295 subjects, with six images acquired per person. The test set achieved 77.1% sensitivity, 70.2% specificity, and an AUC of 78.5%. Differing from previous methods that use basic techniques to merge information from different modalities, [PERSON] et al. [30] trained a deep learning model that uses an attention module to combine information from feature extractors. A five-layer CNN was used as a feature extractor. By merging activations in different layers through this module, a richer combination of information was achieved, resulting in a reported AUC of 96.8% in the test set. This study used 5266 OCT and fundus images to classify 38 Alzheimer's disease (AD) patients, 29 mild cognitive impairment (MCI) patients, and 50 healthy subjects for training and testing the model. Such a high number of images was reached by using 3D OCT and using augmentation techniques for fundus images. Apart from multi-modal techniques, single-modal techniques were also introduced. [PERSON] et al. [21] developed a deep learning model to screen AD patients from fundus images. EfficientNet-b2 network was used as feature extractor. Multiple fundus images were taken for each person, with images of both eyes. A total of 12,949 images from 3888 people were used. The features were concatenated to obtain the final result. The model evaluated multiple test sets, and the average performance was 90.4% sensitivity, 93.5% specificity, and an 80.6% AUC. In another study, [PERSON] et al. [23] built a classification model to distinguish between AD transgenic mice and wild-type mice from OCT images. A modified
## 3 Types of Augmented Reality Applications
### Projection-Based AR  
Projection-based AR, also known as Spatial Augmented Reality (SAR), is a branch of AR that aims to embed virtual information in the physical world in a seamless and intuitive manner. Unlike other AR techniques that require users to look through a display screen (for example, a tablet, smartphone, or smart glasses). SAR uses digital projectors to overlay graphical information directly onto physical objects in the user's environment [36]. This method allows for interactivity by letting users interact with the projected images using hand gestures or touch, for example, a system might use computer vision or other sensing technologies to detect when a user touches a projected button, allowing the user to interact with the AR content in a natural way [37]. The main objective of this approach is to make the interaction between humans and digital information more natural by aligning virtual objects with physical ones. This results in a more immersive and intuitive experience as users are not required to shift their focus between a screen and the environment [38].  
Currently, there are systems that implement this type of AR technology that projects computer-generated information onto fossil specimens, allowing researchers to visualize and study the fossils in new ways. For example, they can project a reconstructed skin surface onto a fossil skull to study the relationship between the skull's shape and the creature's external appearance [26]. Another interesting development that makes use of this technology is \"The Virtual Mirror\" [14], which is a system that projects medical imaging data onto a patient's body, providing a guide for surgeons during operations. The system also allows the surgeon to interact with the projected images using hand gestures, creating a more intuitive interface for viewing and manipulating the medical data. Other applications that involve projection-based AR are industrial training and maintenance, architecture and design, entertainment and gaming, etc. [39].  
Despite the technological advances in this type of AR, there are a few disadvantages and challenges that technologies of this type face [40]:  
* Lighting Conditions: The visibility and quality of the projections can be significantly affected by the lighting conditions in the environment. Developing projection techniques that can adapt to varying lighting conditions is an active area of research.
* Surface Properties: The color, texture, and shape of the projection surfaces can affect the appearance of the projected images. Future work includes developing algorithms that can account for these surface properties to ensure accurate and consistent projections.
* Tracking and Calibration: Accurate tracking of the user's viewpoint and the physical objects in the environment is essential for maintaining alignment between the virtual and physical objects. Achieving this in dynamic environments where objects may move is a challenging problem.
## 2 Materials and Methods
### Extraction and Determination of Carotenoids  
The extraction and determination of carotenoids in citrus juice sacs were conducted using the methods described previously [24]. Pigments were extracted from the juice sacs using a hexane/acetone/ethanol (50:25:25, _v/v_) as extraction solvent containing 10% (_w/v_) magnesium carbonate basic. The organic solvents were completely evaporated under vacuum by rotary evaporator at maximum 35 \({}^{\circ}\)C. Afterwards, the extracts were saponified with 8 mL 20% (_w/v_) methanolic KOH and 12 mL diethyl ether containing 0.1% (_w/v_) 2,6-di-tert-butyl-4-methylphenol. Then, water-soluble extracts were removed by adding NaCl-saturated water. The pigments, repartitioned into the diethyl ether phase, were recovered and evaporated to dryness. Subsequently, the residue was redissolved in 5 mL of a tert-butyl-methyl-ether (TBME)/methanol (1:1, _v/v_) solution. After filtered through a 0.22 \(\upmu\)m syringe filter (Shimadzu GLC, Tokyo, Japan), 1 mL of solvent were completely evaporated and redissolved in 100 \(\upmu\)L TBME/methanol (1:1, _v/v_) solution. The samples were analyzed by using a reverse-phase HPLC system (Jasco, Tokyo, Japan) fitted with a YMC Carotenoid S-5 column (Waters, Milford, MA). In this study, all-_trans_-violaxanthin, 9-_cis_-violaxanthin, lutein, and \(\beta\)-cryptoxanthin, and \(\beta\)-carotene were detected at 452 nm (Supplementary Materials, Figure S1c). The contents of \(\beta\)-carotene, \(\beta\)-cryptoxanthin, lutein, all-_trans_-violaxanthin, and 9-_cis_-violaxanthin were estimated by the standard curves and expressed as \(\upmu\)g g\({}^{-1}\) of fresh weight [24]. The total carotenoids were calculated by summing all identified carotenoids. Carotenoid quantification was performed in three replicates.
## 3 Results and Discussion
### Antioxidant Capacity  
In the determination of the DPPH radical binding activity, it was observed that the antioxidant capacity is also affected by the solvents, with the extract prepared with acetonitrile showing the highest antioxidant capacity by both FRAP and DPPH methods. The results are shown in Table 2. This could explain that components extracted with solvents of higher polarity are less effective at binding the DPPH radical than their lower polarity counterparts. However, changes in the polarity of solvents alter their ability to leach certain antioxidants, thus affecting the measurements [23]. This tendency is also evident in the present case, as acetonitrile has a relatively low polarity (0.460) and was found to be the best solvent. In contrast, ethanol has a higher polarity (0.654) and, therefore, inefficiency [41].  
The obtained data suggest that the garden blackberries have excellent antioxidant potential due to their high polyphenol and anthocyanin content. The total antioxidant concentration values agree with [PERSON] et al.'s results, where the total antioxidant concentration of blackberries averaged 150 umol ascorbic acid/100 g dry matter (2.64 g ascorbic acid/100 g dry matter) [42].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Extraction Solvent** & **E\% (DPPH Assay)** & \
\begin{tabular}{c} **FRAP** \\ **(mg AAE/100 g DW)** \\ \end{tabular} &
\begin{tabular}{c} **FRAP** \\ **(mg AAE/100 g FW)** \\ \end{tabular} \\ \hline \hline ACN & 76.4078 \(\pm\) 1.43 & 3547.68 \(\pm\) 154.76 & 481.06 \(\pm\) 20.98 \\ Met & 63.1294 \(\pm\) 3.45 & 2455 \(\pm\) 141.31 & 332.92 \(\pm\) 19.16 \\ A & 71.9624 \(\pm\) 3.04 & 2853.13 \(\pm\) 413.98 & 386.88 \(\pm\) 56.13 \\ Et & 47.2017 \(\pm\) 2.47 & 1682.22 \(\pm\) 357.37 & 228.11 \(\pm\) 48.46 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The antioxidant capacity of blackberry extracts, obtained with different solvents, measured by three different methods.  
Figure 3: Comparison of standard deviations for different solvents and methods.  
The standard deviations also follow the trend in the present case. Figure 4 shows that the highest standard deviation is for acetone, which is due to its higher volatility in an aqueous mixture, in comparison to the other used solvents.
# Emulsions Stabilized with an Electrostatic Complex of
###### Abstract  
Oil/water (O/W) emulsions were prepared using the complex of quartermized cellulose nanofiber (QCNF) and octanoyl gelatin (OC\(-\)Gel) as an emulsifier, and the effect of pH value on their stability was investigated. OC\(-\)Gel was prepared through a condensation reaction, confirmed by \({}^{1}\)H Nuclear Magnetic Resonance (NMR) and Fourier Transform Infrared (FT\(-\)IR) spectroscopy. It reduced air/water interfacial tension more effectively than unmodified gelatin. The complexation degree of OC\(-\)Gel and QCNF, measured by optical density, showed its maximum at a QCNF/OC\(-\)Gel mass ratio of 1/8 when the pH value of the medium was 7.4, and it increased in a saturated manner with increasing pH value. The signals of QCNF were found in the FT\(-\)IR and X\(-\)ray diffraction spectra of the complex, suggesting that the positively charged CNF were included in the OC\(-\)Gel\(-\)based complex. The complex formed a rough surface with smooth debris because the surface roughness of the complex aggregation reflected that of both QCNF and OC\(-\)Gel aggregation. QCNF could stabilize oil droplets to form a Pickering O/W emulsion. The complex of QCNF/OC\(-\)Gel was also a good emulsifier. QCNF and the complex were as potent as OC\(-\)Gel in emulsifying mineral oil in water. Most of the droplets fell within 5-25 \(\upmu\)m, regardless of what the emulsifier was. The emulsion stabilized with OC\(-\)Gel increased in its oil droplet size more than two times in 20 days at all the pH vales tested (pH 3, 5, 7.4, 9), whereas the emulsion stabilized with QCNF remained almost constant in size during the same period regardless of the pH values. The droplet size of emulsion stabilized with the QCNF/OC\(-\)Gel complex did not change appreciably when the pH value was 5, 7.4, and 9. The complex seemed to act as a capsule wall and prevent the coalescence of the droplets. However, it increased dramatically due to the coalescence at pH 3, possibly because the complex could be dissolved under a strong acidic condition.  
emulsion; quartermized cellulose nanofiber; octanoyl gelatin; electrostatic interactions; complex pH\(-\)dependent stability +
Footnote †: journal: Applied Sciences
# Hybrid GA-SOCP Approach for Placement and
## 1 Introduction
The main problem with these methods is that they have multiple tuning parameters that significantly affect their performance. Additionally, they do not ensure the optimal global solution to the problem. In [28], a methodology for optimal sizing of multiple devices, such as wind turbines, photovoltaic sources, and batteries in hybrid AC/DC grids was provided, which considered the life cycle cost and the life span of the renewable energy resources in the objective function. After the revision mentioned above of the state-of-the-art, this research's main contributions can be summarized, as follows:  
* The reformulation of the problem of the optimal placement and sizing of DGs in DC networks that allows transforming the original mixed-integer nonlinear programming (MINLP) model into a mixed-integer second-order cone programming (MI-SOCP) one. * The solution of the proposed MI-SOCP model with a hybrid master-slave optimization methodology, where the master stage is guided by a genetic algorithm to determine the best locations for the DGs, while the slave stage allows for solving the optimal power flow problem by using the SOCP formulation via interior point methods to determine the best sizes of the DGs. * The computational results demonstrate that the hybrid GA-SOCP approach has an efficiency higher than 70% to find the global optimal solution in both test feeders analyzed. In addition, the proposed approach presents better solutions than commercial solvers available in GAMS and classical metaheuristics such as genetic algorithms and particle swarm optimizer, and derived approachesIt is worth mentioning that the master-slave hybrid GA-SOCP approach has not previously been proposed in specialized literature for addressing the problem of the optimal placement and sizing of DGs in electrical networks, even if these are under AC or DC paradigms of operation. This situation was identified as a gap in the scientific literature that this research tries to fill. The remainder of this document is organized, as follows: Section 2 presents the original MINLP formulation of the problem of the optimal placement and dimensioning of DGs in DC networks by highlighting its nonlinearities and non-convexities. Section 3 presents the proposed hybrid optimization method, which is composed of a conic reformulation problem of the optimal power flow problem in DC networks and the classical GA. Section 4 exposes the main characteristics of the test feeders, which are composed of 21 and 69 nodes with radial structures, where the possibility of installing three DGs is considered. Section 5 presents all of the computational validations of the proposed hybrid GA-SOCP approach and their comparisons with nonlinear solvers available in GAMS and master-slave optimization approaches that are based on genetic algorithms and particle swarm optimization methods and its variants. Section 6 shows the concluding remarks derived from this work and some possible future researches in the area of distribution system optimization.
# Multimodal Fall Detection Using Spatial-Temporal Attention and Bi-LSTM-Based Feature Fusion
## 5 Experimental Evaluation
### Performance Result of the Proposed Model with UP-Fall Dataset  
The integration of the Bi-LSTM and CA model using sensor data achieved higher accuracy than the other two models, so we conducted experiments using a combination of the Bi-LSTM-CA and GSTCAN systems in a multimodal system. The performance accuracy of each is shown in Table 4.  
The results of classifying sensor data using a combination of the Bi-LSTM-Channel Attention and GSTCAN systems are shown in Table 4. Using a combination of the Bi-LSTM-Channel Attention and GSTCAN features, we obtained an average accuracy of 99.09%, precision of 97.06%, recall of 97.18%, and F1-score of 96.99%.
# Dynamic Cooperative Spectrum Sensing Based on Deep Multi-User Reinforcement Learning
###### Abstract  
Dynamic spectrum access (DSA) has been considered as a promising technology to address spectrum scarcity and improve spectrum utilization. Normally, the channels are related to each other. Meanwhile, collisions will be inevitably caused by communicating between multiple PUs or multiple SUs in a real DSA environment. Considering these factors, the deep multi-user reinforcement learning (DMRL) is proposed by introducing the cooperative strategy into dueling deep Q network (DDQN). With no demand of prior information about the system dynamics, DDQN can efficiently learn the correlations between channels, and reduce the computational complexity in the large state space of the multi-user environment. To reduce the conflicts and further maximize the network utility, cooperative channel strategy is explored by utilizing the acknowledge (ACK) signals without exchanging spectrum information. In each time slot, each user selects a channel and transmits a packet with a certain probability. After sending, ACK signals are utilized to judge whether the transmission is successful or not. Compared with other popular models, the simulation results show that the proposed DMRL can achieve better performance on effectively enhancing spectrum utilization and reducing conflict rate in the dynamic cooperative spectrum sensing.  
dynamic spectrum sensing; reinforcement learning; collaborative strategy +
Footnote †: 2021: 11, 1884. [[https://doi.org/10.3390/app11041884](https://doi.org/10.3390/app11041884)]([https://doi.org/10.3390/app11041884](https://doi.org/10.3390/app11041884))
# Modeling Non-Equilibrium Dynamics and Saturable Absorption Induced by Free Electron Laser Radiation
###### Abstract  
Currently available X-ray and extreme ultraviolet free electron laser (FEL) sources provide intense ultrashort photon pulses. Those sources open new exciting perspectives for experimental studies of ultrafast non-equilibrium processes at the nanoscale in condensed matter. Theoretical approaches and computer simulations are being developed to understand the complicated dynamical processes associated with the interaction of FEL pulses with matter. In this work, we present the results of the application of a simplified three-channel model to the non-equilibrium dynamics of ultrathin aluminum films excited by FEL radiation at 33.3, 37 and 92 eV photon energy. The model includes semi-classical rate equations coupled with the equation of propagation of the photon wave packets. X-ray transmission measurements are found to be in agreement with present simulations, which are also able to shed light on temporal dynamics (in the fs range) in nano-sized Al films strongly interacting with the photon pulse. We also expanded our non-linear model, explicitly including the two-photon absorption cross-section and the effect of including electron heating for reproducing transmission measurements.  
X-ray free electron laser; saturation phenomena; nonlinear optics Article