esa-sceva/satcom-chunk-collection huggingface dataset:
# Experimental Approaches in Delineating mTOR Signaling
## 4 Identification and Characterization of mTOR Signaling Pathway Components
### Hypothesis-Driven Studies in Identification of mTOR Signaling Components  
4.1 Connection of TSC2/TSC1 with mTOR Was through an Observation That Both TSC2/TSC1 and mTOR Control S6K Phosphorylation  
TSC2 and TSC1 form a heterodimer to suppress cell proliferation. Although TSC2/TSC1 have been tightly associated with cell size and proliferation control in mice [77; 78] and Drosophila [79; 80], the connection of TSC2/TSC1 with mTOR signaling has remained elusive till 2002. [PERSON] et al. found that overexpression of TSC2/TSC1 in HEK293 cells decreased phosphorylation of mTORC1 substrate S6K but not the mTORC2 substrate Akt, whereas siRNA-mediated TSC2 depletion in HeLa cells increased phosphorylation of S6K but not Akt, supporting a role of TSC2 in suppressing mTORC1 activity in cells [81].  
Furthermore, TSC2 and TSC1 overexpression suppressed 4E-BP1 and S6K1 phosphorylation in U2 OS cells. S6K1 mutants resistant to rapamycin (constitutively active) could not be suppressed by this TSC2/TSC1 ectopic expression, suggesting that mTOR mediates the suppressive effects of TSC2/TSC1 on S6K phosphorylation [82].  
In addition, _TSC2-/-_ MEFs displayed dramatically increased S6K phosphorylation at basal levels, which could be reversed by re-expressing WT, but not two TSC disease-associated mutant forms of TSC2, suggesting that _TSC2_ loss promotes mTORC1 activation [83]. However, how TSC2/TSC1 connects to mTORC1 remained elusive until Rheb filled in.
## 4 Security Analysis of the Proposed Protocol
### Formal Security Proof  
Formal security analysis is an effective method for demonstrating the semantic security of the proposed protocol. It employs formal mathematical techniques to prove whether the proposed protocol satisfies the desired security properties within a specific security model. In this type of analysis, the initial step involves defining a formal model that describes participants, furthering the DY adversary's capabilities. Subsequently, security properties need to be precisely defined. Lastly, the desired security properties are proven to be satisfied within the defined security model via mathematical, logical deduction.
###### Abstract
However, a gap in our knowledge remains concerning whether the neural SNR is capable of accounting for the variability in hearing outcomes with varying NR strength among the same individuals. If the neural SNR indeed captures the essence of individual noise tolerance, it should illuminate the variability observed in outcomes with stronger NR processing, which is characterized by more noise attenuation and more severe speech distortions. This would validate the neural SNR as a reliable measure of individual noise tolerance and enhance our understanding of complexities in NR outcomes associated with personal characteristics in response to the conflicting effects of NR processing. [PERSON], [PERSON] [18] suggested that subjective criteria for noise-tolerance judgments, including _noise annoyance_, _speech interference_, _loudness_, or _distraction_, could differ depending on an individual's hearing status; people with normal hearing seemed more inclined to the aspects of noise itself (i.e., _noise annoyance_), whereas people with hearing impairment gave more weight to how the noise interfered with understanding speech (i.e., _speech interference_). Likewise, the cortical measures of noise tolerance (i.e., neural SNR) encompass the respective evoked responses to target speech onsets and noise onsets [31]. Our recent work on the neural SNR indicated different weighting of cortical processes between people with normal hearing, whose efficiency in inhibiting noise mainly predicted speech-in-noise performance [30], and those with hearing impairment, whose variance in perceptual performance was primarily explained by responses to target speech onset [34]. Notably, both subjective noise annoyance and cortical active suppression of background noise gauged using neural SNR together emerged as a significant factor in noise tolerance among people with normal hearing [18; 30]. Considering both cortical and subjective measures together raises the pivotal question of whether these measures are reflective of the same aspects of individual noise tolerance (e.g., _noise annoyance_), and whether they collectively offer a comprehensive understanding of the variability in NR outcomes. The present study aimed to investigate whether the variability in hearing outcomes with varying NR strength would be accounted for by individual noise tolerance using cortical and subjective measures, addressing the need to develop novel measures to characterize individual characteristics that predict benefits from NR strategies. We hypothesized that both cortical and subjective measures of noise tolerance would correlate with NR outcomes. Further, we expected to find that these measures would capture different facets of individual noise tolerance and collectively provide a comprehensive understanding of how noise tolerance impacts individual NR outcomes.
# YOLO-SAR: An Enhanced Multi-Scale Ship Detection Method in Low-Light Environments
## 4 Results
### Dataset Preprocessing
As shown in Figure 8, the grayscale distribution reveals that while images captured under normal lighting exhibit values evenly distributed across the higher range, the low-illumination images demonstrate a shift towards lower values, indicating a reduction in brightness following preprocessing. Figure 9 illustrates a comparison of histograms for pixel distributions across the HSV channels under normal lighting and low-illumination scenes. In the H channel (hue), the hue values of low-illumination images cluster in the lower range, with most frequencies approaching zero. By contrast, the hue distribution in the original dataset under normal lighting is more uniform. The histogram of the S channel (saturation) shows that low-illumination images possess lower saturation compared to images captured under normal lighting. In the V channel (value), it is evident that the brightness values of low  
Figure 8: Comparison of grayscale histograms under normal light and low light. Figure 7: Sample images from the Lowship dataset (the images obtained from Figure 6 after preprocessing). illumination images predominantly fall within the lower range, further confirming the decrease in luminance. Figure 9: Comparison of pixel distribution histograms of HSV color gamut under normal light and low light. The analysis mentioned above demonstrates that the processed dataset exhibits notable differences in brightness, hue, saturation, and grayscale characteristics compared to the original dataset, thereby validating the effectiveness of preprocessing strategies in simulating low-illumination environments.
## 3 Results
### Fixed Satellite Selection Experiment
(**a**) Comparison of average DGDOP iterative changes across different algorithms throughout the entire flight. (**b**) Comparison of DGDOP across different algorithms and all satellite selections at each observation epoch. suggesting that NSWOA maintains good precision compared to classical algorithms. Although it slightly lags behind GWO and NSGA-II, the difference is within 1%. Furthermore, for epochs with DGDOP values greater than 180, the number of such epochs is fewer than those of NSGA-II and PSO, indicating that NSWOA maintains good optimization precision in most epochs and can still perform excellently in cases of occasional degradation. Under conditions where dynamic user computational resources are limited and a fixed satellite selection strategy is employed, the NSWOA can still meet the dynamic satellite selection requirements in terms of both optimization results and speed.
## 2 Proposed Multi-Objective Formulation  
This section presents our proposed multi-objective mathematical formulation for the ISHOP-U. Equation (1) (\(f_{1}\)) computes the ISHOP-U final purchase cost objective function from their original publication in [1]. In \(f_{1}\), given a total of \(n\) products and \(m\) stores, the components \(s_{i,j}\) represent the quantity to be bought in the store \(i\) for the \(j\)-th product and the components \(c_{i,j}\) represent the price of the \(j\)-th product in the store \(i\)-th. The delivery price of every \(i\)-th store is represented by \(d_{i}\), and the binary variable \(y_{i}\) guarantees that the delivery price is summed up once, i.e., \(y_{i}=1\) if one product or more are bought in the store \(i\). The \(s_{i,j}\) are constrained by product availability in stores and the number of item units required.  
\[f_{1}=\sum_{i=1}^{m}\left(\left(\sum_{j=1}^{n}s_{i,j}c_{i,j} \right)+y_{i}d_{i}\right) \tag{1}\] \[f_{2}=f_{1}\cdot\alpha \tag{2}\]  
Equation (2) (\(f_{2}\)) is our second proposed new objective function. In \(f_{2}\), \(\alpha\in[0,1]\) represents the percentage of cash-back or reward points on the final purchase. From the above formulation, maximizing \(f_{2}\) increases the final purchase cost, which is undesirable, and minimizing the final purchase cost decreases \(f_{2}\), which is also undesirable. Therefore, being a multi-objective optimization problem, the objectives are in evident conflict. This work uses a 5% cash-back return for the final purchase, i.e., \(\alpha=0.05\).
## 3 Results and Discussion
### Characterization of the Nanoparticles Containing Zinc Complexes
#### 3.2.5 Evaluation of the Creams
Skin Irritancy Test  
Skin irritation test showed that the prepared creams did not produce any irritation, redness, or sign of erythema on any of the volunteers to whom they were applied. 'In Vitro' Evaluation of the Antibacterial Activity  
The antimicrobial activity of the creams was evaluated by measuring the width of the inhibition zones formed around the tested samples (Figure 7) of the ZnNPs against the tested microorganisms. The tests showed that sample S1 (whichdoes not contain either VLSENPs or ZnNPs), sample S2 (that contains only the VLSENPs) and sample S3 (that contains the smallest amount of ZnNPs-0.20 g) exhibited no antimicrobial activity. Except for samples S1-S3, all the other samples, S4 (that contains 0.30 g ZnNPs), S5 (that contains 0.40 g ZnNPs) and S6 (that contains 0.50 g ZnNPs), showed an antimicrobial  
Figure 7: Antibacterial activity of the prepared creams against _Staphylococcus aureus_ (**a**), _Methicillin-resistant Staphylococcus aureus_ (**b**), _Escherichia coli_ (**c**) and _Candida albicans_ (**d**). effect that correlated with the amount of ZnNPs. The best antimicrobial efficacy was found for the cream samples S5 and S6, which inhibited all the tested microbial cultures. The inhibition zones obtained at the testing of the sample S5 against the Gram-positive bacteria _Staphylococcus aureus_ (3.6 \(\pm\) 0.3 mm, Figure 7a) and _Methicillin-resistant Staphylococcus aureus_ (3 \(\pm\) 0 mm, Figure 7b) as well as against the Gram-negative bacteria _Escherichia coli_ (3 \(\pm\) 0 mm, Figure 7c) they were similar size and decreased slightly when testing _Candida albicans_ yeast (2.3 \(\pm\) 0.3 mm, Figure 7d), (Table 4). The antimicrobial effect of the sample S6 manifested itself identically against both Gram-positive (4.3 \(\pm\) 0.3 mm) and Gram-negative species (4.3 \(\pm\) 0.3 mm) but decreased significantly against _Candida albicans_ (2.6 \(\pm\) 0.3 mm). IC50 values of the ZnNPs incorporated in cream formulations against the tested microorganism were 7.318 mg/g cream (for _Staphylococcus aureus_), 9.37 (for MRSA), 9.683 mg/g cream (for _Escherichia coli_) and 9.338 mg/g cream (for _Candida albicans_). The antibacterial activity of ZnNPs could be explained by several mechanisms [42; 43]. They act simultaneously or in several steps by blocking the bacteria's ability to form biofilms, damaging the cell membranes and releasing the cytoplasmic contents, by interaction with various cellular components, by generation of reactive oxygen species (ROS) such as hydroxyl radical, hydroperoxide radical and superoxide anion radical that exhibit a bactericidal action [6; 16].
## 4 Results and Discussion  
Through the video monitoring station (May 2009) important changes in the morphology of the estuaries have been observed. The most important being the appearance of a new channel, which is visible through Camera 1. Thanks to this camera, we have been able to observe the evolution of the channel, from its birth, up to its stage of equilibrium (Figure 10).  
Figure 9: Selecting the region of interest (red square—left) and obtaining the rectified image (right).  
Figure 11a shows the four selected transects for the initial growth phase in which the secondary channel has not yet developed its entire length. While Figure 11b shows the location of the transects once the secondary channel has developed its head until it connects with the main channel.  
Figure 10: Development of the secondary channel as of June 2010 up to January 2013.  
As a result of digitalization, we obtained the pixelated coordinates (u, v) that form part of the borders of the channel at each transect. The same mathematical process was applied to this set of points for the rectification of images, so as to transform the coordinates into the equivalent coordinates of real space (x, y, z).  
After the restoration work was completed by the end of October 2009, and after eight months of the continuous ebb and flow of the tide, the channel started to become visible as of 15 June 2010. During these first instances of life (June and July 2010, Figure 10) the channel showed us two branches, of which only the one situated on the left area of the image managed to develop.  
Also, during the first four months of the life of the channel (Figure 10), we can see how its development is characterized by rapid \"incisional narrowing\" [11] in the sense of the longitude of the current. This phenomenon can be explained from the point of view of the shear stress distribution. The shear stress for a given section has a maximum value within the central area of the channel, which falls to zero within its edges [12]. In this way, the erosion tends to concentrate in the centre of the channel during its incision, causing the channel to narrow while it increases its depth.  
The narrow erosion process continued until the transportation of the lateral sediments from the walls of the channel slowed enough to halt the degradation and narrowing of the channel [11]. The channel began to increase its width as of this moment, as it cut its way into the estuary.  
This last behavior can be observed in the new channel as of the first equinoctial spring tides in September 2010. This is when the channel began to increase its width to a greater extent than before. As of November of the same year, the channel began to develop the connection of its head with the main channel, until about the month of March 2011 and subsequent months (Figure 10).
# Color Harmonization, Deharmonization and Balancing in Augmented Reality
## 3 Materials and Methods
### Color Space Conversion  
The first step of the proposed algorithm is the color space conversion. First, a set of images is obtained by means of the device camera by taking pictures of the real scenario. Next, each image is analyzed to read colors from pixels of images in RGB color space that expresses a color by using three channels: red (R), green (G), and blue (B), defined in the range [0,255]. Afterwards, RGB values are converted into HSV ones that consist of hue, saturation, and value channels. In detail, the RGB values are first divided by 255 to change the range from [0,255] to [0,1], and then converted from RGB to HSV color space through equations reported in [61]. These new values are used to compute the color histogram of the original images using L bins (typically L = 360). Each color's frequency is normalized according to the highest one of the whole distribution.
# Microchannel-Embedded D-Shaped Photonic Crystal Fiber-Based Highly Sensitive Plasmonic Biosensor
## 3 Performance Analysis and Numerical Results
The amplitude sensitivity is defined as below:  
\[\mathrm{S}_{A}(\mathrm{RIU}^{-1})=-\frac{1}{\alpha(\lambda,n_{a})}\frac{ \partial\alpha(\lambda,n_{a})}{\partial n_{a}} \tag{7}\]  
where \(\alpha(\lambda,n_{a})\) is confinement loss for analyte RI of \(n_{a}\) at a particular wavelength \(\lambda\). \(\partial\alpha(\lambda,n_{a})\) indicates change in propagation loss at wavelength \(\lambda\) for \(\partial n_{a}\) change in analyte RI [11]. The amplitude sensitivity depends only on \(\partial\alpha(\lambda,n_{a})\) and \(\alpha(\lambda,n_{a})\) because \(\partial n_{a}=0.01\) in our analysis. Figure 4b shows the amplitude sensitivity spectra of different analytes' RI varying from 1.33 to 1.43. The maximum amplitude sensitivity increases gradually with the increase in the analyte RI from 1.33 to 1.42. Such as, for the increase in analyte RI from 1.33 to 1.34 (i.e., \(\partial n_{a}=0.01\)), the maximum amplitude sensitivity is found as 106.64 RIU\({}^{-1}\) and the change in RI from 1.34 to 1.35 (i.e., \(\partial n_{a}=0.01\)) results in the maximum amplitude sensitivity of 145.89 RIU\({}^{-1}\). However, the maximum amplitude sensitivity decreases form 1439.00 RIU\({}^{-1}\) to 343.59 RIU\({}^{-1}\) with the change in the analyte RI from 1.43 to 1.44. Table 1 shows peak amplitude sensitivities for analytes' RI from 1.33 to 1.43. Table 1 shows that the highest amplitude sensitivity is 1439.00 RIU\({}^{-1}\) with a resolution of 6.94 \(\times\) 10\({}^{-06}\) RIU for change in analyte RI from 1.42 to 1.43. The resonant wavelengths for different analytes' RI in the range of 1.33 to 1.44 and the polynomial fitting are shown in Figure 4c. From resonant wavelengths, it is found that the R-squared value of the sensor is close to unity (i.e., R\({}^{2}\) = 0.99559), which indicates the sensor is highly linear. The full width at half maximum (FWHM) is found from the loss spectra of different analyte RI and plotted in Figure 4d and shown in Table 1. The FWHM decreases with the increase in the analyte RI and reaches a minimum for RI = 1.39 and increases again with the increase in analyte RI. The FWHM is used to characterize the sensor in terms of the figure of merit (FOM), which is a ratio of wavelength sensitivity to the FWHM. Figure 4d shows that FOM increases with the increase in the analyte RI. The highest FOM is 839 for analyte RI = 1.43 as shown in Table 1.
# Exploring the Use of Landmarks to Aid Pedestrian Wayfinding
## 5 Discussion
### Implications of the Research
#### 5.1.1 Enhanced Wayfinding Strategies  
This study demonstrates that incorporating landmark imagery into navigation aids can significantly improve wayfinding performance. Participants who used photographs of landmarks made fewer navigation errors and hesitations compared to those relying on traditional map-based systems. This suggests that landmarks serve as effective cognitive tools that enhance spatial awareness and route-following capabilities, particularly in complex pedestrian environments.
## 3 Results and Discussion
### Structural Health Monitoring via Ib-Value Analysis  
As discussed in the previous section, the Ib-value can be used to diagnose the state of the structure by determining the frequency at which a higher-amplitude signal appears as a result of crack propagation. Figure 9 presents an example of an amplitude distribution and Ib-value calculation for a 0 h specimen. Labels #1-4 are the same as those in Figure 8b. Amplitudes were calculated based on Ib-values within a specific range around the mean value (a1 = 10, a2 = 5). The Ib-values continuously decrease, unlike the \(b\)-value. Our previous \(b\)-value analysis of a GFRP [30] determined that increasing \(b\)-values stem from the complex failure behavior of FRP, which creates many microcracks, even when they are propagating. Hence, the Ib-value is more suitable than the \(b\)-value for SHM of FRP structures.  
Figure 10 shows the Ib-values over the lifetime of the specimens. The parametric values were calculated after successive generation of 10 AE hits (e.g., 1 st-10 th, 1 st-20 th... 1 st-100 th). The Ib-values continued to linearly decrease until the end of the experiment. The maximum Ib-value in the experiment corresponded to the beginning of crack growth,  
Figure 8: Acoustic emission (AE) hit distribution. (**a**) Cumulative AE hits and (**b**) AE occurrence rate (hits per second) for the 0 h specimen.  
Figure 9: Example cumulative hits and Ib-values.  
and the minimum value corresponded to specimen failure. The trends were quite similar between the 0 and 100 h specimens, but the starting Ib-value was lower for the 200 and 300 h specimens. Notably, the Ib-value behavior of all specimens linearly decreased and converged to the same value at final failure. Thus, the behavior of an environmentally degraded GFRP can be predicted based on the Ib-value behavior of the pristine specimen.
## 3 Results and Discussions
### Robocasting of Lattice, Dense and Bi-Layer Parts
#### 3.1.6 Fracture Mechanism Analysis  
Figure 9 shows the in-situ compression test configuration of the samples inside the ESEM, in which the stress was applied perpendicular to the ceramic filaments. The sample was held between two grips and the observations were done parallel to rods direction.  
Figure 10 shows the condition of a sample after loading and the observation of a few sudden load drops. It can be seen that the failure mode consisted of longitudinal cracks aligned with the load axis running through the structure. Cracks were initiated from the external surface of the rods, nearby their intersections (image B). In fact, while loading, the area underneath two intersecting rods was under compression, while the perpendicular \"free\" rod fraction was probably submitted to flexural strength (with the upper parts in traction, as evidenced by the appearance of cracks indicated by white arrows in Figure 10). Increasing the loading increased the number of longitudinal cracks and the cracks propagated through the layered structure.  
Figure 9: Test configuration as well as sample placement within the in-situ compression test device.  
It was observed that even after the development of several cracks, the structure preserved a certain mechanical resistance. This condition was revealed on stress-strain curves as the saw-tooth pattern (Figure 8A). Figure 11 shows the remaining parts of the lattice structure after the final fracture. It was reported that columnar structures that remain oriented along the load direction were responsible for such extended load-bearing capacity [40]. Of course, by load removal, these structures collapsed with no connection between the pillars able to provide any mechanical integrity.  
Figure 12 illustrates the propagation of two parallel cracks during the compression test of the porous structure captured by ESEM. Image A shows two parallel cracks close to the intersection of the rods at the compressive loading of 2139 N. It can be seen that the cracks propagated with both inter (A) and intragranular (B) paths. Image B illustrates the condition of the cracks after increasing the applied loading to 2211 N. The dashed circle highlights the progression of the cracks' tip resulting in the crack's growth. However, because sample strain led to the redistribution of the applied force, the opposite sides of the previous cracks became closer. Image C shows the condition of the same cracks a few minutes after Image B, just before the final failure. Further growth of the cracks and bigger opening of the cracks' faces are visible. However, the final compressive loading dropped to 2083 N due to the stress relief by the cracks' growth.  
Figure 11: Remaining columnar parts of a collapsed 3D porous structure within the in-situ compression test device.  
Figure 10: (**A**) Longitudinal cracks running through the structure of a porous scaffold during the compression test observed by ESEM, (**B**) a higher magnification image of a representative of the cracks.