esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Results and Discussion
### Insert Helix Contributes to the Binding Affinity of CDC42 for IQGAP1 Grd  
The question arises as which regions on CDC42 could bind GRD if we can now exclude the switch regions. A region/site that has attracted our attention is the IH of CDC42 for valid reasons. We have shown that IQGAPs bind to RAC-like and CDC42-like proteins but not to the other members of the RHO family [26] and the IH consistently is a highly variable region among the RHO GTPases (Figure 3A) [15]. Several CDC42-binding proteins, including RHOGDI1, p50 GAP, FMNL2 and IQGAP2 have been shown to contact the IH [10; 12; 17; 18; 20]. Thus, mutational analysis of the CDC42 IH was performed, using four different single residue mutations and a quadruple mutation (Figure 3A and Table S1). Note that variable residues were replaced in CDC42 by the corresponding residues of RAC1. Most remarkably and in sharp contrast to the SW mutations (Figure 2), all IH mutations abolished GRD1-CDC42 interaction irrespective of the nucleotide-bound states of CDC42 (Figures 3B and S6), which underlines the central role of CDC42 IH in GRD binding. The scenario was rather different for C794, which binds mGDP-bound CDC42 with 3-fold and mGppNHp-bound CDC42 with 20-fold lower affinities (Figure 3B). Thesedata are consistent with the recent observation by [PERSON] and colleagues that the CDC42 IH reveals nucleotide-dependent conformational flexibility [16].  
The data from fluorescence polarization could be verified via pull-down assay. The binding pattern of CDC42 IH mutants with endogenous IQGAP1 followed the same pattern, displaying no binding for A130K and 4 xIH and very weak binding for S124D (Figure 3C). Generally, binding could be observed much stronger for GppNHp-bound than for GDP-bound CDC42 variants, supporting the pull-down data shown above (Figure 1C).  
Several published studies have shown that mutations of the CDC42 IH impact their properties in binding IQGAPs. [PERSON] et al. (1999) have shown that IH deletion in CDC42 impairs its binding affinity for the effectors, in particular IQGAP1 C794 [53]. [PERSON] et al. (2008) investigated the impact of the IH mutations in CDC42Q61L on IQGAP1 C794 binding [56]. Consistent with our findings, they observed a slight decrease in C794 affinity for CDC42Q61L with A130K or N132K. Moreover, [PERSON] et al. also applied the CDC42Q61L variant and suggested the IH together with switch I region to be mainly responsible for its binding to the _ex_-domain of GRD (GRDex) of IQGAP2 [54].
# Tracing Student Activity Patterns in E-Learning Environments: Insights into Academic Performance
## 3 Methodology and Results
### The Markov Chain Model, the Graphs, and the Eigenvectors
#### 3.2.3 The Eigenvectors  
To identify the eigenvector associated with the primary eigenvalue of the transition probability matrices, linear algebra was employed. In Markov chains, the principal eigenvalue of a transition matrix generally equals 1, and its corresponding eigenvector reveals the steady-state probabilities of the chain. Consequently, over time, the likelihood in each state converges towards the values specified by this eigenvector, irrespective of the starting state. For instance, the eigenvector of the transition probability matrix for the student with ID 67 (the related graph is presented in Figure 1b), is [0.13056, 0.33838, 0.01848, 0.30592, 0.01582, 0.00526, 0.18558] mapped to list [Assignment, Course, Folder, Forum, Glossary, Other, Quiz]. The output lists the states sorted by their steady-state probabilities. For example, if we take into consideration this student, Course has the highest steady-state probability of approximately 0.34. Forum follows with a probability of about 0.31, and so on. The significance of these probabilities is that, given a large number of transitions (or as time approaches infinity, in a theoretical sense), the student will spend approximately these percentages of time in each respective state.  
For example, the state labeled \"Other\", with an almost negligible probability, represents the scenario where the student is least likely to be found in the long term, typically involving engagement with testbed-focused activities. This analysis provides valuable insights into which events are likely to be the predominant or most common states over time for all students, as per the model. Such insights can serve as valuable tools for enhancing user engagement within Moodle and for refining instructional design strategies. In the case of the student with ID 67, as mentioned above, potential actions could include placing the icon for testbed activities in a more prominent location within Moodle or increasing reminders about their existence through frequent email notifications.
# Planar Microwave Resonant Sensors: A Review and Recent Developments
###### Abstract  
Microwave sensors based on electrically small planar resonant elements are reviewed in this paper. By virtue of the high sensitivity of such resonators to the properties of their surrounding medium, particularly the dielectric constant and the loss factor, these sensors are of special interest (although not exclusive) for dielectric characterization of solids and liquids, and for the measurement of material composition. Several sensing strategies are presented, with special emphasis on differential-mode sensors. The main advantages and limitations of such techniques are discussed, and several prototype examples are reported, mainly including sensors for measuring the dielectric properties of solids, and sensors based on microfluidics (useful for liquid characterization and liquid composition). The proposed sensors have high potential for application in real scenarios (including industrial processes and characterization of biosamples).  
microwave sensor; differential sensor; dielectric characterization; microfluidics; electrically small resonators; biosensors +
Footnote †: journal: Applied Sciences
## 3 Results  
### Characterisation and Quantification of CB in Dispersion  
The signals at all MALLS detector angles (except the 7\({}^{\circ}\) angle) were used to calculate the CB particle size distribution (PSD) using a random coil fit in the software. For this exercise, 75 ng/mL dispersions were used for PX 80 and PX 85 and 125 ng/mL for N550 and N772. PX 80 and PX 85 exhibit a similar PSD, as do N550 and N772 with each other. In general, the PSD of PX 80 and PX 85 was narrower than that of N550 and N772. The results are summarized in Table 3. The radius of gyration (\(\mathrm{r_{g}}\)), also known as root mean square radius, describes the distribution of mass around the centre of gravity of a particular particle. This calculation is based on the angular variation of the MALLS signal intensities and it is independent of the shape of the particle. A geometrical radius can be calculated to take into account the shape of the particle [16]. In Table 3, the recalculated geometrical sizes under the assumption of compact spheres (\(\mathrm{d_{sphere}}\)) and random coils (\(\mathrm{l_{coil}}\)) are also displayed. It must be noted that the real shape of CB (aciniform aggregates) deviates somewhat from these ideal geometrical forms.  
In the AF4 fractogram, CB elutes at a low cross-flow rate. PX 80 and PX 85 particles elute between t = 17-32 min, N550 and N772 between t = 20-32 min. The dispersant 96% ethanol did not cause any interfering signals in these time windows.  
For all CB dispersions, a series of dilutions from a fresh 100 mg/L stock dispersion were prepared at 0 (blank), 5, 10, 25, 50, 75, 100, and 125 ng/mL in 96% ethanol. The AF4 fractograms were integrated at all detection angles (except the 7\({}^{\circ}\) angle) of the MALLS detector to obtain the peak areas that are directly proportional to the injected mass. The CB particles were completely dispersed in 96% ethanol with no sediment, thus the weighed mass of CB corresponds to the mass of CB in the dispersion. The CB standard dispersions could therefore be used for the calibration of the MALLS output by the peak areas.  
Identical concentrations of PX 80 and PX 85 dispersions caused higher signal intensities in the AF4 fractogram than both rubber grades, N550 and N772. Whilst even the lowest concentrated dispersion of PX 80 and PX 85 (i.e., 5 ng/mL) could be differentiated from the blank and the next higher concentration (10 ng/mL), the N550 and N772 dispersions at 25 ng/mL were the lowest concentrations that could be clearly differentiated for these rubber grades. Consequently, 5 ng/mL was set as the detection limit of the device for PX 80 and PX 85, and 25 ng/mL for N550 and N772 for a 1 mL injection volume. The correlation between the scattered light intensities and the injected mass of CB is exemplified in Figure 3, with the AF4 fractograms being recorded at the 92\({}^{\circ}\) detector angle, and in Figure 4 through the calibration curve that was obtained from the integration of the total peak area at all angles. The results for all CB dispersions are summarized in Table 4.
# Passive Microwave Imagers, Their Applications, and Benefits: A Review
## 3 The Applications, Benefits, and Impacts of PMWIs on Society
Weather disasters in 2020 cost USD 258 billion, including USD 63 billion in damages, with more than 80% of these damages occurring in the U.S. (Figure 8) [117]. [PERSON] et al. reported that the effect of weather variability on the U.S. economy can amount to 3.4% (USD 485 billion) of the Gross Domestic Product (GDP) in 2008 dollars. The authors also found that economic sectors such as agriculture, communications, construction, finance-insurance-real estate (FIRE), manufacturing, mining, retail trade, services, transportation, utilities, and wholesale trade are statistically significantly sensitive to at least one measure  
Figure 7: The U.S. billion-dollar disaster events from 1980 to 10 June 2024 (source: figure created by [149]). of weather variability, with mining (oil, coal, and gas extraction) being the most sensitive one [148]. As a countermeasure to such adverse effects of weather variability and climate change on the U.S. economy, the NWS has developed the Impact-based Decision Support System (IDSS) as a tool for providing forecast advice and interpretation to support decision-making when people's lives are at risk [150]. Figure 9 depicts how IDSS would be beneficial to decision-makers; with the use of IDSS, forecasts and warnings will be integrated into the decision-making process collaboratively [138]. Specifically, [PERSON] et al. analyzed the benefits of IDSS by comparing two storms in New York City (December 2010 and January 2016). They found that IDSS, combined with proactive decision-making, saved over USD 90 million in the energy sector, reduced ground transportation recovery time by five days, and saved USD 17 million in aviation. The benefits were greatest for major storms but diminished for extreme events where improved decision-making could only prevent a few impacts. Additionally, not all of these improvements were solely due to better decision-making, as factors such as societal preparedness also played a role [138]. Figure 8: Global economic costs from weather disasters from 1950 to 2020 (source: figure created by [117; 151]). Figure 9: Value chain of IDSS information (source: figure created by [138]). The impacts of these weather disasters can be mitigated by the use of Earth observations, including PMWI data. These data play a crucial role in improving forecasting of natural disasters by providing timely information. However, quantifying their impact remains a challenge due to the lack of information on how these data actually benefit society, how they are being used by different sectors, and the varying levels of subjectivity in the methodologies developed to quantify them.
# Wavelength Selection for Satellite Quantum Key Distribution
## 5 Results and Discussion
### Adverse Atmospheric Conditions  
Waterford winter skies often experience a range of adverse weather conditions. Considering historical hourly weather reports and model reconstructions for noon on 21 December [69], there is more than a 60% chance of mostly cloudy or overcast weather and a 39% chance of rain falling throughout the day. In this section, the performance of each wavelength is compared in these unfavourable conditions. Several low-level cloud/weather (<3 km) conditions were considered, including radiative fog with 500 m visibility, drizzle rain clouds, and haze, which is defined here as fog with a 5 km visibility. Other cloud types such as stratus, stratocumulus and moderate rain had even greater attenuation than drizzle rain clouds, so they are not included here. Figure 7 shows the transmittance and spectral radiance plotted as a function of wavelength for Figure 7a 5 km visibility haze, Figure 7c 500 m visibility radiative fog, and Figure 7e drizzle rain clouds. Research supports the advantages of LWIR FSOC systems for propagation through turbulence, as well as fog and other low visibility conditions [29; 30; 70]. In addition to reduced visibility, strong adverse atmospheric conditions can lead to serious (and random) deformations of the beam, resulting in a strong turbulence regime. Theoretical studies have been performed on quantum communications in moderate-to-strong turbulence, where atmospheric conditions can be detrimental to optical signals [17]. Positive key rates were obtained despite severely degraded signals and high optical losses. In strong turbulence, beam spread dominates pointing errors and beam wandering. All regions of the spectrum are attenuated by haze, but the LWIR region is much less affected. This improvement is even more evident when operating through fog and rain, where transmission is low in the LWIR but almost zero in all other regions. Analysis of these plots alone would conclude that the LWIR is the best region for QKD through adverse weather conditions.  
Next, the secret key rate is calculated across the spectrum for the three adverse atmospheric conditions, as shown in Figure 7b,d,f. Surprisingly, the LWIR has the worst performance in all cases. Indeed, the only condition that permits positive rates is haze, with usable rates confined to wavelengths shorter than the LWIR. Wavelengths in the visible, NIR, and SWIR regions have higher secret key rates in haze due to their small spot sizes, which allows for tight spatial filtering. Despite the LWIR having the highest channel efficiency in adverse conditions, it also has the largest solid angle field of view and largest receiver requirement, resulting in the highest background probability, which consequentially yields the lowest secret key rate.
# On Pseudorandomness and Deep Learning: A Case Study
## 4 DL-Based Distinguisher
### Model Definition  
Our proposal for a DL-based distinguisher is based on a convolutional Siamese neural network, whose architecture is depicted in Figure 2. It consists of two main parts: a _feature learning_ part and a _similarity learning_ part. For the sake of conciseness, in the description, a certain familiarity with neural networks is assumed. The reader with no background can consult any introductory textbook to the field.  
The feature learning part learns a function \(c\), which maps our binary inputs into a high-level feature space. Let \(s_{i}\) and \(s^{\prime}_{i}\) be the input sequences of the network, and let \(F_{i}\) and \(F^{\prime}_{i}\) be the outputs of the feature learning part, that is, \(F_{i}=c(s_{i})\) and \(F^{\prime}_{i}=c(s^{\prime}_{i})\). The function \(c(\cdot)\) is implemented by a convolutional neural network (CNN, for short) [34,35]. Previous experience in the field, intuition, and trials and errors, guided us to the following choices: in each sub-network, our CNN architecture consists of three one-dimensional convolutional layers, with filters and kernels of varying sizes: in order, 16 filters with size (\(5\times 1\)), 32 filters with size (\(5\times 1\)), and 64 filters with size (\(3\times 1\)). The network applies a ReLU activation function to the output feature maps, followed by a one-dimensional max-pooling layer with size (\(2\times 1\)). Each layer of the sub-networks receives the output from its immediate previous layer as its input, and passes its output (as input) to the next layer. Higher-layer features are derived from features propagated from lower layers. As  
Figure 1: A simple Siamese neural network architecture.  
Figure 2: Our convolutional Siamese neural network architecture.  
the features propagate to the highest layer, the dimensions are reduced, depending on the size of the kernel. The number of feature maps is increased to better represent the input sequence and ensure similarity accuracy. The output of the final convolutional layer is then flattened into a single vector.  
The similarity learning part learns how similar two input sequences are. Precisely, the output vectors of the feature learning part, \(F_{i}\) and \(F_{i}^{\prime}\), are used as the inputs to the fully-connected feed-forward layers. The fully-connected layers are then followed by one more distance layer, which computes the L1 distance metric, by summing the absolute differences of the components of the vectors. The distance value is then passed to a single sigmoidal output unit, which maps the value into the interval \([0,1]\). Score 0 denotes no similarity, while score 1 denotes full similarity.
# Hyaluronic Acid-Based Wound Dressing with Antimicrobial Properties for Wound Healing Application
## 4 HA-Based Devices Combining Natural Antimicrobial Agents  
Among natural substances acting as antimicrobial agents, antimicrobial peptides (AMP) have recently been appreciated as a potential new class of antimicrobial drugs, being expected to solve the problem of antibiotic drug resistance. They exert their activity in a quite unique way, permeabilizing bacteria cell membranes: pores or structural defects are formed, even at low concentrations. A specific AMP called Tet213 combined with substrates of alginate, HA and collagen showed how such substrates' dressings exhibited antimicrobial activity against _E. coli_, _MRSA_ and _S. aureus_, inhibiting or killing bacteria in infected wounds. Briefly, a solution of three polymers was prepared in deionised water with concentrations being 1.2%, 0.5%, and 0.6% (\(w/v\)), respectively, and then Tet213 was added. Thus, samples were lyophilized to obtain the wound dressing. Therefore, those wound dressings granted an effective healing, enhancing collagen deposition and improving angiogenesis thanks to the fast release of AMP [32] (Table 1). Similarly, curcumin is a natural phenolic substance extracted from the rhizome of _Curcuma longa_ and is claimed to possess antibacterial properties. Moreover, curcumin exhibits photodynamic properties, which have been exploited in order to enhance its antimicrobial efficacy on infected wounds [33]. Therefore, the effects in vivo and in vitro of curcumin-modified HA (250 kDa) substrates on antimicrobial and wound healing properties have been studied. For such a study, HA-cur conjugate was prepared by stirring for 1 h at room temperature a solution of 1% (\(w/v\)) of HA solubilized in 1:1 water/DMSO with N-Dicyclohexylcarbodiimide (DCC) and 4-dimethylaminopyridine (DMAP). The bactericidal efficacy of HA conjugate was evaluated in the dark and after exposure to blue light [34] (Table 1). They explained the bactericidal effect of curcumin by asserting that it inhibits cellular proteins, FtsZ and sortase A, both involved in bacterial cytokinesis and cell adhesion [35; 36]. The potential of a wound healing biomaterial consisting of a curcumin-grafted HA-modified pullulan was evaluated. To produce this biomaterial, pullulan was dissolved in 10 mL DMSO and then was stirred at 50 \({}^{\circ}\)C with succinic anhydride and DMAP. After precipitation in anhydrous ethanol, precipitation was dissolved in ethanol, then dialyzed and lyophilized. The antibacterial effect was tested on _S. aureus_ and _E. coli_ by the agar diffusion method and it was shown how the antimicrobial activity of curcumin-grafted polymers on Gram-positive bacteria is better than that of Gram-negative bacteria (Figure 4) [37] (Table 1).
## 4 Discussion  
### Data Analysis in the Time Domain  
The analysis of stabilographic values, such as the COP velocity and the COP movement range, constitutes the basis for evaluating a human being's ability to keep balance [1; 5; 16]. The introduction of oscillation into the virtual scenery in the AP direction led to a statistically significant increase in values in both the cases of the COP velocity and the COP movement range in the AP direction in relation to the measurements with OE (Figure 4A,B). Similar conclusions were drawn in previous research conducted by the authors [1]. The increase in these values results from the balance of the body following the oscillating scenery, which was confirmed by the authors' preceding investigations, where FFT analysis proved the presence of an additional harmonic at a frequency equal to the frequency of the scenery movement. The investigations also demonstrated the presence of additional harmonic at other frequencies, which may indicate a change in the balance-keeping strategy or destabilization of the tested person [20; 21].  
In the measurements with the change in frequency (0.7/1.4 and 1.4/07), one could observe a greater interquartile distribution for the data obtained after the frequency change (0.7_after and 1.4_after) in the case of the COP velocity values in the AP direction in comparison with the data obtained before the frequency change (0.7_before and 1.4_before) (Figure 4A,B). This result may mean that the change from one frequency to another exerts a destabilizing impact and somehow forces the tested participant to search for a new strategy to maintain balance.  
Analyses in the time domain and additional analyses in the frequency domain make it possible to determine if and how virtual disturbances influence changes in the balance-keeping strategy [21] and body-balancing strategy during movement [22]. However, the frequency analysis is limited to the discovery of movement components of a cyclic character. In addition, such an analysis is burdened with noise related to spectral leakage and incomplete signal periods in the measurement window. The application of computational methods drawn from stock exchange analyses makes it possible to supplement the performed analyses with signal components that do not have a cyclic character [13; 14].