esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Feature Wavelengths Based on NRS  
The neighborhood rough set (NRS) algorithm was used to optimize the spectral feature variables. It can be seen from reference [23] that the range of neighborhood size (\(\delta\)) was from 0 to 1 and the step size was usually set as 0.05; the range of variable accuracy (\(\beta\)) was between 0.5 and 1, and the step size was set as 0.05 or 0.1. Based on the parameter values in reference [26], the combination of \(\beta\) = 0.75 and \(\delta\)= (0.05, 0.10, 0.15, 0.20) was named NRS-a, and the combination of \(\beta\) = 0.85 and \(\delta\) = (0.05, 0.10, 0.15, 0.20) was named NRS-b; these were used for optimizing characteristic wavelengths, and the results are shown in Table 2.  
The 16, 13, 12, and 10 wavelength variables of different \(\delta\) were selected by NRS-a and their distribution is shown in Figure 4. The analysis showed that the selected wavelengths were mostly distributed near the main absorption peaks and there were certain commonalities among them in different \(\delta\). Thus, the nine wavelength variables with common characteristics (1057 nm, 1116 nm, 1204 nm, 1298 nm, 1354 nm, 1450 nm, 1454 nm, 1651 nm, 1762 nm) were further refined based on the optimization results of NRS-a for the establishment of classification models.  
For NRS-b, the 12, 10, 8, and 6 wavelength variables of different \(\delta\) were selected separately and their distribution is shown in Figure 5. Through contrast analysis, the number of feature wavelengths selected by NRS-b had decreased and there were dif  
Figure 2: Frequency plot of paddy samples in calibration and test set.  
ferences with the optimization results of NRS-a, but they were also distributed in the vicinity of the main absorption peak. In addition, the eight wavelength variables (1086 nm, 1148 nm, 1234 nm, 1276 nm, 1400 nm, 1567 nm, 1651 nm, 1754 nm) were condensed based on the optimization results of NRS-b for the establishment of classification models. It was necessary to further verify through the established classification model whether the selected characteristic wavelengths can express the storage state of paddy.  
Figure 4: Selection of optimal wavelengths by NRS-a.  
Figure 3: Spectral curves of paddy samples for different storage statuses.
## 1 Introduction
In this paper, we study the issues mentioned above and propose a robust Correlation Filters based Scale Adaptive (CFSA) visual tracker, which could well deal with drifting and scale variation and realize near real-time tracking. In CFSA tracker, we propose a modified object proposal generator based on the EdgeBoxes [26] to generate candidate proposals that are more suitable for visual tracking than the existing generators. The modified object proposal generator can obtain a set of a few hundred top-scoring candidate object proposals in tens of milliseconds. Then, two complementary features (e.g., the HOG and color naming features) are effectively integrated to train a robust and discriminative correlation filter, and the tracker based on the trained correlation filter is implemented on each of top five candidate proposals. The location with the maximum response is the estimated position of the target object. Furthermore, in order to estimate the scale of the target object more accurate and efficient, we propose to use the water flow driven minimum barrier distance (MBD) algorithm [27] to detect object on an image patch centered at the estimated position of the target object. The scale of the target object is obtained within milliseconds by combining the detected object and the estimated position. In order to alleviate the drifting problem caused by occlusion and its surrounding background, an online updating schema with Peak-to-Sidelobe Ratio (PSR) [15] is employed. Experimental results on OOTB (Online Object Tracking Benchmark) [2] and OTB-100 [4] benchmark datasets demonstrate that the CFSA tracker achieves a promising performance in robustness and effectiveness compared with several state-of-the-art trackers in the literature, while operating at about 18.5 frames per second (fps). The reminder of this paper is organized as follows. In Section 2, the related work is briefly reviewed. In Section 3, the whole pipeline and the detailed description of the CFSA tracker are presented. The experimental results compared with several state-of-the-art trackers are presented and discussed in Section 4. Finally, conclusions are given in Section 5.
# Carbon Nanotubes-Based Hydrogels for Bacterial Eradiation and Wound-Healing Applications
## 5 CNT-Based Nanocomposites for Wound Healing
The results showed that even small (250 \(\mu\)L/mL) concentrations of CNT can remarkably increase the cell migration in hydrogels. However, high concentrations did not induce the cell migration further. The schematic representation of cell migration assay and their results for the average cell migration index is shown in Figure 5e. Gelatin-grafted-dopamine (GT-DA) and poly(dopamine) coated CNT (CNT-PDA) hydrogels with chitosan were engineered to present multifunctional injectable hydrogel for antibacterial wound healing [156]. The hydrogel also demonstrated sustained delivery of doxycycline. Poly(dopamine)-coated CNT was incorporated due to its conductive, photothermal, and antibacterial properties. CNT is well known for its excellent electrical, mechanical, and thermal properties.
# Digital Identity in the EU: Promoting eIDAS Solutions Based on Biometrics
## 4 Biometric Identification as a Unique Opportunity  
Although biometric authentication technologies are not a recent topic [24; 25; 26], the widespread use of smartphones opened new development and commercial opportunities for these solutions. It has been foreseen by [27] that by 2022, more than 5.5 billion mobile devices will be used to verify, through biometric authentication, 1.37 trillion transactions. Thanks to the evolution of integrated devices like HD and depth cameras and LIDAR (LIght Detection And Ranging is a laser system that allows measuring the distance between a surface and the laser source, measuring the time for the reflected light to return to the receiver) and faster network speeds, the opportunity for commercial innovation has led to the development of new biometric identity verification solutions that are more secure and with improved customer experience.  
Commercial identity verification providers have been able to produce applications that read data from the chip inside electronic identity documents thanks to the constant expansion of smartphone functionality, particularly features in devices that use Near Field Communication (NFC). Reading the identifying features directly from the chip, including the digital image of the document's owner's face, drops the risk of photo substitution forgery and improves data capture accuracy [28].  
In the following, an innovative authentication scheme exploiting mobile phones, eID cards, and face recognition is proposed. A possible implementation is illustrated in Figure 3, where the current eID card-based authentication schema is integrated with biometric data. The authentication procedure begins when the user accesses the SP's website using a standard browser on a mobile or desktop device (steps 1-2). During the login phase, a query is redirected to the IdP, which requests biometric authentication via the ID APP (step 3). The ID APP exchanges data with the Identity Provider to confirm the user's identity (steps 4-5). The ID APP retrieves the gallery (step 6b) and probe data (step 6c)
## 3 Results
### Mathematical Modeling of the Cell Shape  
Experiments show that the changes in the cell shape, characterized by cell area and circularity measurements, are associated with changes in the length of cortical actin filaments. In order to understand how actin dynamics affects the shape of single cells placed on differently charged substrates, we developed a vertex-based model as described in Section 2.3. Here, we focus only on surfaces with PPAAm (moderately positive charges) and PDADMA (highly positive charges).  
Experiments show that irrespective of the substrate charge, cells initially, i.e., 1 h after seeding on the substrate, have a higher circularity, i.e., closer to 1. Therefore, in our simulations, for the initial state of the cell, we used the values for parameters listed in Table 1 such that the cell shape has a circularity close to 1 (Figure 6a). We chose a high ratio of \(K_{A}\)\(L^{2}/K_{P}\), where \(L\), as described in Section 2.3, is the typical diameter of an osteoblastic cell, so that the cell cross-sectional area at the initial time point is fixed at the value \(A_{0}\). In our simulations, we did not consider the full time-dependent evolution of the cell shape, but only the final force-balanced state resulting from a change in the model parameter values. The cell morphology in the final state is defined by its area and circularity. Cell circularity is calculated using the relation given by Equation (1).  
Figure 5: FilaQuant-based quantification of actin filaments in MG-63s after 1 and 24 h on negatively charged Ti (blue), moderately positively charged PPAAm (green), and highly positively charged PDADMA (orange). (**a**) Number of actin filaments. (**b**) Total filament length. (**c**) Mean filament length. (**d**) Maximum filament length. Note the significantly higher number of filaments on moderately positively charged PPAAm after 1 h and the resulting increased total filament length. Whereas the highly positively charged surface PDADMA leads to a drastically diminished filament number and length after 24 h, there are no significant differences in filament length and number between Ti and PPAAm. (Statistics are shown in Table 2, \(n=10\) cells per surface).  
Figure 6: Model to study the influence of actin dynamics on the shape of cells placed on charged surfaces. **(a)** Cells were initialized as hexagons with an area (in non-dimensional units) of 500 and a circularity of 0.9. The blue-colored circle inside the polygon represents a cell nucleus which is not part of the model and is only shown here for representative purposes. This state corresponds to the cells on a PPAAm (model +) and PDADMA (model ++) substrate after 1 h. The shape of the cells results from the energy minimization of Equation (3) for a given set of values of parameters listed in Table 1, using a Monte Carlo-based algorithm. **(b)** Cell area (in non-dimensional units) after 1 h (red circles) and 24 h (green diamond for model + and blue diamond for model ++). Each data point for cell area is the mean of cell areas obtained from ten individual simulation runs. **(
###### Abstract  
The aim of the study was to assess the clinical effects of periodontal healing using a Romanian pharmaceutical compound of marine fish extract (Allfittop(r)). Adults with periodontal disease were included in the study group. Gingival inflammation, the degree of tooth mobility, and probing depth (PD) were recorded for each patient before and after therapy. Patients were divided into two groups: group I--after scaling and root planing (SRP), patients followed therapy with marine fish extract, Allfittop(r), group II--SRP therapy alone. Statistically significant differences between groups in terms of gingival inflammation reduction (\(p\) = 0.045) were found. Tooth mobility reduction, as well as PD improvement, were also noticed after the therapy (\(p\) = 0.001), but no statistically significant differences among PD reduction rates were found (\(p\) = 0.356). Allfittop(r) has proven a certain therapeutic efficiency in the treatment of periodontitis in terms of reduction in the clinical signs of inflammation and tooth mobility.  
p 2022 2021 9 January 20221 11 January 2022 10.3990/app1202067 23 December 2021 9 January 2022 11 January 2022 10.3990/app1202067 23 December 2021 9 January 2022 11 January 2022 10.3990/app1202067 23 December 2021 9 January 2022 10.3990/app1202067  
and the acceleration of tissue healing or bone regeneration [9]. The main objective of non-surgical periodontal regeneration treatment involves the obtainment of a biologically active complex of natural compounds [10; 11; 12] aimed at assisting periodontal healing. In this context, natural biological chemical compounds are increasingly preferred as treatment alternatives. One of these compounds is Alflutop(r), a Romanian pharmaceutical bioactive concentrate of marine fish, from the Black Sea, which is widely used in the treatment of degenerative osteoaticular diseases, inflammatory rheumatism or post-traumatic osteitis [13]. In vitro, Alflutop(r) has antioxidant and anti-inflammatory activity [14; 15] and improves the proliferative status of chondrocytes [16]. Additionally, studies proved its effectiveness in reducing inflammatory cascade progression by inhibiting the release of pro-inflammatory cytokines such as interleukins (IL 6 and IL 8) and tumor necrosis factor-alpha (_TNF_*) and is similar to the anti-inflammatory effect of dexamethasone [14]. According to previous research, the pharmaceutical properties of Alflutop(r) also include analgesic effects [17]. Past studies used the marine fish extract (Alflutop(r)) in dentistry, in endodontics, and in chronic periapical periodontitis after cleaning and shaping the root canal. The results showed the regenerative properties of Alflutop(r) used in endodontic temporary root canal treatment [18].  
The aim of this research was to assess the clinical effects of the marine fish extract Alflutop(r) in periodontal therapy. Substances composing Alflutop(r) are natural and, according to the manufacturer, aimed at enhancing the proper metabolism at the joint's level and repairing the cartilage.
# A Novel Chipless Hybrid RFID Sensor for Metal Crack Detection
## 2 Materials and Methods
However, for vertical cracks, the variation in depth (\(R^{2}=0.0303\)) does not have much effect on the shift in resonant frequency, as shown in \[a=\frac{F}{\sqrt{1+\frac{2h}{\pi a\epsilon_{r}}F[ln(\frac{\pi F}{2h}+1.7726)]}} \tag{1}\]0 In general, the resonant frequency shifts with different crack orientations and dimensions. This shift can be calculated using a normalized magnitude of resonant frequency \(\Delta\)\(F_{r}\) as given in Equation (5), where \(f_{r}^{crack}\) is the resonant frequency of the crack, and \(f_{r}^{0}\) is the resonant frequency of the tag with no cracks. \[a=\frac{F}{\sqrt{1+\frac{2h}{\pi a\epsilon_{r}}F[ln(\frac{\pi F}{2h}+1.7726)]}} \tag{1}\]1  
\[a=\frac{F}{\sqrt{1+\frac{2h}{\pi a\epsilon_{r}}F[ln(\frac{\pi F}{2h}+1.7726)]}} \tag{1}\]2 RCS and resonant frequencies for vertical crack depths. \[a=\frac{F}{\sqrt{1+\frac{2h}{\pi a\epsilon_{r}}F[ln(\frac{\pi F}{2h}+1.7726)]}} \tag{1}\]3 RCS and resonant frequencies for horizontal crack depths. \[a=\frac{F}{\sqrt{1+\frac{2h}{\pi a\epsilon_{r}}F[ln(\frac{\pi F}{2h}+1.7726)]}} \tag{1}\]4 RCS and resonant frequencies for vertical crack widths. The \(\Delta F_{r}\) for both vertical and horizontal cracks is calculated and tabulated. If the numerical sign of the \(\Delta F_{r}\) is positive, then the resonant frequency is increasing, and if it is negative, then there is a reduction in the resonant frequency. Figure 9 shows a tabulated form presenting (a) the horizontal crack width, its corresponding resonant frequency, and \(\Delta F_{r}\), which is the quantitative shift of frequency with respect to the frequency with no crack. Figure 9, Figure 9, and Figure 9 show frequency tables for the horizontal crack depth, vertical crack width, and vertical crack depth, respectively. For horizontal cracks, there is a linear shift in the resonant frequency to lower frequencies as the crack width increases. For horizontal cracks, the detection sensitivity is approximately \(-2.82\%\), which is a 117 MHz shift per 1 mm increase in the crack width, and \(-4\%\), which is 164 MHz/1 mm for the horizontal depth. Similarly, for the vertical crack, the sensitivity is approximately \(2\%\) or 84 MHz/1 mm for the vertical crack width, which means the frequency shift increases with increase in the width and \(0.76\%\) or 31 MHz/1 mm variation approximately for the vertical crack depth. For the horizontal crack dimensions, the variations are linear. However, for the vertical crack depth, there is not much variation in \(\Delta F_{r}\), but to some degree, the presence of a vertical crack with width can be detected.
## 3 Development History of Navigation Signal Authentication Technology
### Terminal Processing  
Since the authentication message/spreading code lags behind the to-be-authenticated signal, there is a risk of spoofing attacks during this time. Terminal processing technology refers to how the user terminal handles the authentication signal.  
1. Message Authentication Processing.  
Taking SBAS navigation message authentication as an example, SBAS requires the integrity alarm time to be 6 s, and the authentication message will lag the integrity message [28; 53]. The authentication MAC will be delayed by at least 1 s, and the key corresponding to the MAC will be delayed by 6 s. For terminal processing, it is faced with the  
Figure 14: Authentication performance indicators.  
problem of whether to perform authentication processing or to use integrity first. Authentication first will cause the integrity alarm to time out. If the integrity is used first, the user's integrity parameter may be forged. Therefore, the processing of message authentication is still a problem that needs to be studied.  
(2) Spreading Code Authentication Processing.  
Compared with the navigation message authentication process, which stores only the navigation message, the spreading code authentication process needs to buffer the signal sample data [16; 63]. Taking the CHIMERA signal as an example, slow channel authentication needs to cache data for at least 180 s. According to the 20 MHz sampling rate and 2-bit quantization, a 7.2 Gbit buffer is needed, and the buffer capacity of the receiver cannot meet the requirement at present. Fast authentication requires the data to be cached for at least 3 s, requires a 120 Mbit cache, and requires low-orbit satellite/5 G network assistance, which involves the co-processing of navigation and communication, which is still under study.
# Data Hiding and Authentication Scheme for Medical Images Using Double POB
## 4 Analysis of Simulation Result
### Hiding Performance
#### 4.2.3 Time Efficiency Analysis  
The experiments are implemented on a computer with a 1.60 GHz Intel i5 processor, 8.00 GB memory, and Windows 10 operating system. Using the POB algorithm, because of the simultaneous compression and re-encryption operations, will lead to a slightly higher time overhead in programming implementation. Table 3 lists the time overhead of using single POB and double POB embedding in different embedding capacities. In the table, it can be seen that although the use of double POB embedding has a slightly larger time overhead, it does not affect the practical application.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{1}{c}{**Test Images**} & \multicolumn{1}{c}{**Embedding Method**} & \multicolumn{1}{c}{**Embedded Capacity (bits)**} & \multicolumn{1}{c}{**PSNR**} & \multicolumn{1}{c}{**SSIM**} \\ \hline
512 \(\times\) 512 & Brain 01 & Encrypted ShareA\({}^{\prime}\) & 52,428 (10\%) & \(\infty\) & 1 \\
512 \(\times\) 512 & Brain 02 & Encrypted ShareB\({}^{\prime}\) & 157,286 (30\%) & \(\infty\) & 1 \\
256 \(\times\) 256 & Pulmonary 01 & Encrypted ShareA\({}^{\prime}\) & 65,536 (50\%) & \(\infty\) & 1 \\
256 \(\times\) 256 & Pulmonary 02 & Encrypted ShareB\({}^{\prime}\) & 104,857 (80\%) & \(\infty\) & 1 \\
180 \(\times\) 180 & Pancreas & Encrypted ShareA\({}^{\prime}\) & 12,960 (20\%) & \(\infty\) & 1 \\
256 \(\times\) 256 & Chest & Encrypted ShareB\({}^{\prime}\) & 19,660 (15\%) & \(\infty\) & 1 \\
300 \(\times\) 162 & Knee & Encrypted ShareB\({}^{\prime}\) & 29,160 (30\%) & \(\infty\) & 1 \\
64 \(\times\) 64 & Hand & Encrypted ShareA\({}^{\prime}\) & 3276 (40\%) & \(\infty\) & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 2: PSNR and SSIM of recovered images under different embedding capacities.
# Search results that are considered mostly satisfying after screening are marked with *
## 3 Results
### Stimulation Parameters  
Table 2 displays the aggregated results from 12 studies where stimulation frequencies and intensity data were available. Different frequencies of stimulation current have been reported, ranging from 50 to 1000 Hz (Table 2). The magnitude of current stimulation in previous studies varied from 20 to 3000 \(\mu\)A; however, the majority of stimulations were administered within the 50 to 200 \(\mu\)A range (Table 2). Several authors noted that excessive current stimulation often induces a bitter taste, while mild stimulation could produce more desirable tastes such as sour, salty, and sweet.  
[PERSON] et al. in 2012 demonstrated that the electrical stimulation of the tongue was capable of evoking sour, salty, and bitter sensations, with sour taste being particularly pronounced under higher current intensities and frequencies [22]. Their system, the Tongue-Mounted Digital Taste Interface, utilized current magnitudes ranging from 20 \(\mu\)A to 200 \(\mu\)A and frequencies from 50 Hz to 1000 Hz to induce different taste sensations. To ensure user comfort, the system employed a constant current source that adapted to individual tongue impedance.  
Augmenting the sensory environment through cross-modal effects, such as altering the temperature and the color of water via LED lighting, were shown to modify participants'taste perceptions. For instance, [PERSON] et al. in 2016 demonstrated that brighter colors often induced perceptions of sweetness, further emphasizing the role of visual cues in taste modulation [44]. Five studies reported that the temperature [22; 30; 34; 36; 45] of the interface was controlled to maximize the outcome. Additionally, two papers [37; 38] mentioned using LED lights to change the color of water in the cup to further modify participants' taste sensations when asked to drink these cups of water.  
Specifically, [PERSON] et al. in 2014 and 2016 introduced the Bottle+ and Spoon+ systems, which combined electrical stimulation with visual augmentation using RGB LED lights [37; 38]. These devices superimposed virtual colors onto beverages or utensils to enhance the sensory experience. For example, sourness was associated with green light and a current magnitude of 120 \(\upmu\)A at a frequency of 600 Hz, while saltiness corresponded to blue light at 40 \(\upmu\)A and 200 Hz. The ability to simulate bitterness was further refined using reversed polarity and red light, available exclusively in the Bottle+ design [38].  
The impact of thermal feedback on human taste sensation was explored by [PERSON] et al. in 2014 and [PERSON] in 2024. They applied specific temperature to the tongue or surrounding skin and found that this could significantly alter flavor perception [34; 36]. Their experiments showed that heating the tongue enhanced sweetness and reduced bitterness, while cooling intensified sour and salty sensations. These findings underscore the potential of integrating thermal stimuli into future designs for a more comprehensive taste simulation.